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Research Article Automatic Segmentation of Colon in 3D CT Images andRemoval of Opacified Fluid Using Cascade Feed ForwardNeural Network

K. Gayathri Devi1 and R. Radhakrishnan2

Dr. N. G. P. Institute of Technology, Coimbatore , IndiaVidhya Mandhir Institute of Technology, Tamilnadu, India

Correspondence should be addressed to K. Gayathri Devi; [email protected]

Received October ; Revised January ; Accepted January

Academic Editor: Giancarlo Ferrigno

Copyright © K. Gayathri Devi and R. Radhakrishnan. Tis is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

Purpose. Colon segmentation is an essential step in the development o computer-aided diagnosis systems based on computedtomography (C) images. Te requirement or the detection o the polyps which lie on the walls o the colon is much needed inthe eld o medical imaging or diagnosis o colorectal cancer. Methods. Te proposed work is ocused on designing an efficientautomatic colon segmentation algorithm rom abdominal slices consisting o colons, partial volume effect, bowels, and lungs. Te

challenge lies in determining the exact colon enhanced with partial volume effect o the slice. In this work, adaptive thresholdingtechnique is proposed or the segmentation o air packets, machine learning based cascade eed orward neural network enhancedwith boundary detection algorithms are used which differentiate the segments o the lung and the uids which are sediment at theside wall o colon and by rejecting bowels based on the slice difference removal method. Te proposed neural network methodis trained with Bayesian regulation algorithm to determine the partial volume effect. Results. Experiment was conducted on Cdatabase images which results in % accuracy and minimal error rate. Conclusions. Te main contribution o this work is theexploitation o neural network algorithm or removal o opacied uid to attain desired colon segmentation result.

1. Introduction

Colorectal cancer occurs either in the colon or in the rectum.Most colorectal cancers initially developed a colorectal polyp,which is growths inside the colon or rectum that may laterbecome cancerous. It affects both men and women mostly above the age o . It is the third most common canceramong men afer prostate and lung cancer []. For women,colorectal cancer is the third most common cancer aferbreast and lung cancers []. Because o this dreadul disease,research in colon segmentation hasacceleratedonly in thelastew years. A common approach involved in the segmentationo colon includes the ollowing three steps.

(i) Air surrounding the body should be removed.

(ii) Air contained in the lungs is masked.

(iii) Segment the colon on the various slices.

But, the above mentioned steps are undamental and do notprovide the desired results in all the scenarios. Te process o colon segmentation is a challenging task. Some o the pointsrelated to the difficulty in colon segmentationare listedbelow.

() Te colon is not the only gas lled structure in theabdomen but there are other organs which are havingthe same intensity values as the colon. Slices adjacentto the colon contain portion o lung which should beremoved.

() Te existence o different areas that have the samehigh C number (intensity) such as bones, air, andcontrast enhancement uid (CEF) should be removedor proper segmentation o colon.

() One nds obstructions such as stools, small bowel,polyps, and residual eces in the colon.

Hindawi Publishing CorporationComputational and Mathematical Methods in MedicineVolume 2015, Article ID 670739, 15 pageshttp://dx.doi.org/10.1155/2015/670739

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Computational and Mathematical Methods in Medicine

() One nds olding and unolding o the colon struc-tures such as haustral olds on the borders o colon.

() Length o the colon and the location o the colon vary or different slices.

Tese difficulties make the segmentation o colon more com-

plicated specially in situation where an automated algorithmshould be implemented.But, in recent years, certain advanced approaches have

been developed to deal with colon segmentation. Most o the segmentation approaches are exploited only on conven-tional colonoscopy, capsule endoscopy, and barium enemaX-ray examination methods. Recently, it is observed thatC colonography uses low dose radiation C scanning toobtain an interior view o the colon (the large intestine)[, ] whereas in endoscopy, insertion o tube in the rectumwill affect the privacy o the patient. Tere is a chanceo abdominal pain and to lessen that a muscle-relaxingdrug may be injected intravenously to relax the bowel and

to reduce the discomort. Tis is not the case with Ccolonography []. Te difference between a polyp and ecalmaterial can be revealed easily by the radiologist.

Te ollowing points clearly describe the advantages o C colonography compared to conventional colonoscopy,capsule endoscopy, and barium enema X-ray examination.

(i) It enables the radiologist to see inside the colonwithout having to insert a viewing instrument, thecolonoscope into the bowel.

(ii) Tis new minimally invasive test provides both Dand D images that can depict many polyps and otherlesions as clearly as when they are directly seen by conventional colonoscopy.

(iii) Elderly patients, especially those who are ill, willtolerate C colonography better than conventionalcolonoscopy.

(iv) C colonography can be helpul when colonoscopy cannot be completed because the bowel is narrowedor obstructed or any reason, such as by a large tumor.

(v) C colonography is less costly than colonoscopy.

Tus, in this work, an automatic segmentation method isproposed or colon segmentation rom abdominal computedtomography (C) images. Since not many works are carriedout or the segmentation o colon with automated process,

this work ocuses on developing an efficient automationsegmentation approach. Te proposed multistage detectionmethod is employed to extract colon based on the colonanatomy inormation. Tis may be a rapid and easy diagnosismethod o colon at an earlier stage andhelp doctorsor analy-sis o correct position o each part and detect the colon rectalcancer much easier. Virtual colonoscopy combines axialspiral C data acquisition o the air-lled and cleansed colonwith -dimensional imaging sofware to create endoscopicimages o the colonic surace []. Tis paper ocuses on theadvantages o computer-aided detection (CAD) techniquesorthe segmentationo colon which will aid the identicationo polyps or the detection o colorectal cancer.

In this proposed methodology, the air packets areextracted rom the input image by Otsu thresholding methodand then the lung slices are extracted rom the input image.Te extracted lung image was ed as input to the cascadedeed orward neural network or prior training o the partial

volume effect region. Te extraction o eature such as the

intensity and the location were processed by correlatingthe input slice with its corresponding ground. Finally, thetiny holes and the bowels o the image are removed by slice difference removal method (SLDR). Tus, the maincontribution o the work is to improve the results o colonsegmentation through cascaded neural network.

Related Works. A number o semiautomated and automatedmethods or the segmentation o colon have been proposed.Bert et al. proposed the automatic segmentation o colonusing D seeded region growing algorithm []. Losnegårdet al. [] proposed a semiautomated method or the seg-mentation o sigmoid and descending colon and the removalo bowels was perormed using ast marching method. Tesegmentation results were evaluated using the parameter dicecoefficient (DC).

LuandZhao[] proposed a method that canremove non-colonic attachments and connect collapsed colonic segmentstogether or the VC examination. Te proposed method ismainly composed by a noncolonic attachment classicationalgorithm and a heuristic connection algorithm. Computer-generated colons are compared with human-generated colonswhich are manually extracted by two radiologists.

Chowdhury and Whelan [] propose a ast and accuratemethod or automatic colon segmentation rom C datausing colon geometrical eatures. Afer removal o the lungand surrounding air voxels rom C data, labeling is per-

ormed to generate candidate regions or colonsegmentation.Te centroid o the data, derived rom the labeled objects, isused to analyze the colon geometry. Other notable eaturesthat are used or colon segmentation are volume/lengthmeasure and end points.

aimouri et al. [] use constrained least-squares ltering(CLSF) technique or preprocessing and both cleansing andstool detection in the colon are done prior to segmentation.Lu and Zhao [] proposed an improved method or colonsegmentation using two widely virtual colon unolding (VU)techniques, the ray-casting technique and the conormal-mapping technique.

Kilic et al. [] proposed an automatic three-dimensional

computer-aided detection system or colonic polyps. Com-puter-aided detection or computed tomography colonog-raphy aims at acilitating the detection o colonic polyps.First, the colon regions o whole computed tomography images were careully segmented to reduce computationalburden and prevent alse positive detection. In this process,the colon regions were extracted by using a cellular neuralnetwork and then the regions o interest were determined.In order to improve the segmentation perormance o thestudy, weights in the cellular neural network were calculatedby three heuristic optimization techniques, namely, geneticalgorithm, differential evaluation, and articial immune sys-tem. Aferwards, a three-dimensional polyp template model

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Branch B

Branch A

3D CT images

2D axial slice

imagesExtraction of air packets

by Otsu’s thresholding

Individual uid packets

extracted in all slices using

trained net le

Extraction of lungs by

detection ofboundaries (OI)

Performing AND operationApplying OTSU based

Binarization of the outputMorphological

Removal of tiny holes (OutF)

Performing AND operationRemoval of bowels by slice

difference method

Concatenation of PVE withthe colon

Step 1

Step 9

Step 3

Step 7Step 6

Step 8

Step 4

Step 2

Step 5

Step 10

Step 11

binary transform (D1)between D0 and D1

(D0)processing (Imo)

F : Te overview o the proposed method.

was constructed to detect polyps on the segmented regionso interest. At the end o the template matching process, the

volumes geometrically similar to the templatewere enhanced.able lists the various methods proposed andthe limitationso that method or the segmentation o colon.

Te abovementionedsection clearly discusses the existing

colon segmentation approaches and its limitations whichaffect the overall perormance o the system.

2. The Proposed Segmentation Scheme

Te overall block diagram o the proposed method is shownin Figure . Abdominal C image in DICOM ormat o size × obtained rom CIA imaging Archive link [] andreal time dataset obtained rom imaging centre are given asthe input to the proposed method. Each dataset consists o millions o voxels with slices ranging rom approximately to which makes the processing o million voxels difficult.So, in this approach, seven D slices out o torso C

scan were considered as training samples or the automaticsegmentation o colon. Te proposed approach starts withidentication o all the air lled regions in D axial slices. Inthis region, colon and noncolon portion should be classied.Tese regions contain bowels, small intestine, lungs, andother random noises. Foreground should be separated rom

the background by removing the air portions that are outsidethe body o the subject. Afer the presegmentation process,each segmented component is labeled as one o the veanatomical categories such as colon, small intestine, mixed(colon and small intestine), stomach or lung, and noise.Mixed class and colon categories are considered as positiveclass. All the others are negative class. Te removal o thenegative class, which is masking o the lungs, is perormedbased on the calculation o the area occupied by differentboundaries and the removal o the bowels is carried out basedon slice difference method.

Te procedure adopted or the extraction o uid lledparts inside the colon was implemented using cascade eed

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: Comparative study o the method proposed with its limitation.

Author Method Limitation

Bert et al. () []Automatic segmentation o colonusing D seeded region growingalgorithm

It is obvious that the most seriousproblem o region growing is thepower and time consuming

Losnegård et al. () [] Semiautomatic segmentation Disadvantage is that it consumes

more time and lesser accuracy

Lu and Zhao () []Noncolonic attachmentclassication algorithm and aheuristic connection algorithm

Tis method could achieve .%coverage o human-generatedcolons, which is o .% higherthan the conventional method

Chowdhury and Whelan () []Automatic colon segmentationrom C data using colongeometrical eatures

Tis approach perorms better andprovides efficient results in colonsegmentation

Kilic et al. () [] Automatic three-dimensional

computer-aided detection systemAverage coverage is about .% o the entire colon

aimouri et al. () [] Constrained least-squares ltering

(CLSF)Applicable only or specic cases,not converged early

(a) Input image (b) Initial segmentation output

F : Extraction o air packets.

orward neural network. Te networks were already trainedto handle the partial volume effect which consist o airlled region, uid lled region, and boundaries (AFB) whichinterconnect the air-uid as shown in Figure (a). Te net-work will handle the partial volume effect, extract the uidregion, and ll the holes using morphological processingapproach. Tus, afer the removal o bowels and the random

noises, concatenation o the extracted partial volume effectregion with the above segmentedcolonsegments providesthenal output.

Te portions o colon segmented by the proposed algo-rithm are compared with the manually segmented colonstructures segmented using IK snap sofware []. Teperormance o the proposed method is evaluated withparameters, namely, accuracy, dice coefficient,sensitivity, andspecicity and is compared with adaptive level set and graphcut methods.

Step : Extraction of Air Packets. In this section, the inputslice is considered, which comprises air packets. Te air

packets are removed using OSU thresholding. Tresholdselection o the input slices is based on OSU thresholdingwhich was introduced in the year []. OSU methodperorms clustering by means o automatic thresholding andreduces the image rom gray scale to binary orm, throughwhich the simple segmentation o image is processed. Initially the histograms o an image along with its probabilities are

calculated or each and every unit in the slice. Tus thisprocess separates the air and nonair regions. Trough this aclass variance is estimated using class probability and classmean o each unit in an image. An optimum threshold valuewill be selected based on the histogram in order to reducethe separability o two classes; hence the two oregroundclasses and the background classes are separated correctly so that intraclass variance is minimum. Tus the intraclass

variance is the variation o pixel values within the same class,which will notvary to a large extent. Te pixelscorrespondingto the air-lled colon are easily ound as a result o thisthresholding, ofen other air-lled anatomies like the lungs,small bowel, and stomachs, as well as the air outside the body,

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(a)

L = 3

L = 4

L = 2

L = 1

(b)

(A) Input image (B) Initial segmentation (C) Iteration 1 (D) Iteration 2 (E) Iteration 3

(c)

F : (a) Sample input slice. (b) Evaluation o continuous segments (). (c) Results o extraction o lungs.

Air

Opacied uid

Bones

A F BInterface layer

(a)

Neural network

Input

Hidden layer

Output layer

Output

1

1

64

1

W

W

Wb

b

(b)

F : (a) Sample slice with partial volume effect. (b) Architecture o the training network.

are included as in Figure or a sample slice. Removal o these noncolon segments is described in the next section.

Te initial segmentation is denoted as IS. Tis initial stepgives a rough approximation o the location o the colon. Teother alternative techniques that can be used are -meansand Fuzzy means clustering [, ].

Te resultant image afer extraction o air uid packetsapproximately is shown in Figure . Tis process gives anapproximate location o the colon.

Step : Extraction of Lungs. Tis module o operation dealswith detecting and extracting boundaries rom a binary transormed image. Tis module is used or tracing theoutermost object (parent) and the holes which reside insidethe outermost object (child). Tese modes are also capable

o detecting parent and child objects in the binary image.Tis algorithm works based on Moore-Neighbor tracing

algorithm modied by Jacob’s stopping criteria [, ]. Tusthe number o boundaries and the number o holes insidethe boundaries are calculated. Tus, this step will generatethe location o the boundaries [] and number o continuoussegments in each slice [].

Step is carried out to remove the lungs or the initialslices in which the coverage area o the lungs is more.Te parameters considered or the removal o lungs are thespecic area and the number o continuous regions in eachslice (). Te various areas in the slices are analyzed by means o varying itsthreshold region. Tis process starts withthe assignment o threshold value = 1. For the initialslices, the segments containing the lung tissue will be more

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and the number o continuous regions will be minimum;hence “” is chosen as the threshold value. Figure , whichhas been considered or explanation, has value = 4.Tere are continuous or connected regions and the majorportion o the slices occupies the lung tissue. Each connectedcomponent is uniquely labelled, which help in locating the

organ o interest. Connected component labelling is used todetect connected regions in binary digital images. Connectedcomponent labelling works by scanning an image, pixel by pixel (rom top to bottom and lef to right) in order toidentiy theconnected pixel region, that is,regions o adjacentpixelswhich share the same set o intensity values. Connectedcomponent analysis is perormed using -neighbourhoodconnectivity. Tus the neighbouring pixels in all eightdirections are checked or connectivity.

Afer perorming the labelling process,the differentprop-erties related to shape o the organs such as Area, Euler Num-ber, Orientation, Bounding Box, Extent, Perimeter, Centroid,Extrema, and Convex hull are checked or each connectivity region. Te parameter considered or the segmentation o abdominal organ is “area.” Te area o all the pixels in animage is calculated by summing the areas o each pixel in theimage. Te specic area o the pixels occupied by the lungswill vary depending on the slice. Te area coverage o thelungsegment will be more or initial slices and will decrease orconsecutive slices. Tus the maximum number o connectedregions () that is to be considered or the segmentation o lungs was, because as thecolon segments starts originating,the segments covered by the lung tissue will decrease; hencethe number o continuous regions () will keep on increasing.For the slices which have the connected region > 18,the above steps were not executed because the probability o occurrence o the lung segments will be minimum and morenumbers o colon segments will start originating.

Tus, based on the above described process, lung regionsare removed as shown in Figure (c) or various iterationswhen the area o lungs is small compared to the colonsegments. For each iteration the connected segments corre-sponding to the lung are identied and nally it is removedas shown in the iteration o Figure (c).

Based on the above eature, the resultant image (, )will produce an output with a value o where the organis segmented and a value o zero elsewhere. Hence the lungregion is extracted here rom the resultant slice andcomparedwith colon segments.

Step : Morphological Processing . Te segmented slices o theabove step will be ollowed by a morphological operation.Tis operation perorms the morphological process by open-ing the single plane or gray scale image structurally. Tisprocess involves image erosion ollowed by dilation whichleads to the smoothening inside the object. It opens the imageobject by means o reerence structural elements speciedby some required size and shape. Tere are two types o structuring elements involved which are at and nonat. Inthis work, the structuring elements used will be at type suchas shapes like disk with a radius o units. Tis particularstructuring element was used because the segment o thecolon will approximate to the size o disk. Ten, air-uid

boundaries are to be identied and extracted using efficientmachine learning approaches to analyze and predict the exactorm o tissue types air, liquid, sof tissue, muscle, and bone.Cascade eed orward back propagation neural network isproposed to extract the individual uid packets which isdiscussed in next step.

Step : Extraction of Individual Fluid Packets. Afer externalair and the preliminary process or the removal o lungsare completed, the identication and extraction o air-uidboundaries are perormed by using the concept o machinelearning. In the C images there are ve tissue types air,liquid, sof tissue, muscle, and bone []. Figure (a) showsone transverse slice which is enhanced with opacied uid.

Te prior training o the partial volume effect regionhas been perormed by using cascade eed orward back propagation neural network. Pattern association probleminvolves storing a set o patterns in such a way that when testdata are presented, the pattern corresponding to the data isrecalled and matched accordingly. Te cascade eed orwardneural network as shown in Figure (b) consists o inputlayer, hidden layer with neuron, and output layer. Teinput processed rom the input layer is added with the weight;each subsequent layer consists o weights coming rom theinput and all previous layers. All layers have biases. Eachlayer’s weights and biases are initialized. In experimentation,the numbers o neurons in hidden layer are increased romone neuron to neurons. Te procedure or the trainingo the neural network is perormed by using the ollowingprocedure.

() In this algorithm, a pattern is presented at the inputlayer. Te neurons at this layer pass the pattern

activations to the next layer neurons, which is actually the hidden layer. Te outputs at the hidden layerneurons are generated using a threshold unctionalong with the activations determined by the weightsand the inputs. Te threshold unction is computedas 1/(1 + exp(−)) where is the activation unction

value which is computed by multiplying the weight vector with the input pattern vector.

() Te hidden layer outputs become input to the outputlayer neurons, which are again processed using thesame saturation unction.

() Te nal output o the network is eventually com-puted by the activations rom the output layer.

() Te computed pattern and the input pattern are com-pared and in case o discrepancy, an error unctionor each component o the pattern is determined, andbased on it the adjustments to weights o connectionsbetween the hidden layer and the output layer arecomputed. A similar computation, still based on theerror in the output, is made or the connectionweights between the input and hidden layers. Teprocedure is repeated until the error unction reachesthe range o the error tolerance actor set by the user.

() Te advantage o using this algorithm is that it issimple to use and well suited to provide a solution to

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(a)

0

Count: 6279Mean: 28723.211Standard deviation:

Bins: 256

Min: 0Max: 65535Mode: 65535 (680)

Bin width: 255.996

65535

25330.150

(b)

F : (a) Te value o the pixels corresponding to the small blue square in the interace layer AFB. (b) Detailed histogram plot o theportion o the sample slice in (a).

all the complex patterns. Moreover, the implementa-tion o this algorithm is aster and efficient dependingupon the amount o input-output data available in thelayers.

Tis theorem uses the knowledge o the prior events topredict the uture events; hence this algorithm can be appliedto our method and the network is trained a priori or thedetection o partial volume effect and can be called in theuture or the detection o partial volume effect in any dataset.Our results reveal that there is a tradeoff between numbero neurons in hidden layer, learning time, and classicationaccuracy.

Te extraction o eature such as the intensity and thelocation was done by correlating the input slice with its corre-sponding ground. Based on the pixel values corresponding tothe pattern o the partial value effect as shown in Figure (a),the assignment o the -bit pixel values or the differentcategory o tissues was made as listed below.

Assign

A = to

\\ or intensity values rom to it isconsidered as region o colon lled with air;

P/M = to

\\ or intensity values rom to itis considered as either muscles or a PVE;

M = to

\\ or intensity value rom to it is considered as muscles;

HI = to

\\ or intensity values rom to it is considered as either bones or liquid.

Duringtraining process, we took different slicesor analysis.Te neural network was trained based on the ollowing

condition. Here truementions the activeregion orextractionand alse represents the region that need not be extracted.

Te target will be true or the existence o Category A,Category A and P/M, and Category A, P/M and HI.

Te target will be alse or the existence o Category M,Category A and M, Category HI, and Category P.

() Te high intensity (HI) region which indicates bothbone region and the liquid region lies in the intensity

value rom to so the only option to ndthe liquid region is thatit lies near PVE and in the bestcase HI lies with both PVE and air region.

() Are per our previous point discussion i HI or Mor P intensities are ound in a window alone thenit is considered as bone and muscle and cannot beconsidered or colon or liquid or PVE.

() I air region and PVE/muscle occur together withouthigh intensity area then it is said to be inactive region.

Te efficiency o the identication o the partial volume effectis evaluated with two parameters mean square error (MSE),the average squared error between the network outputsand the target outputs, and regression. Te perormancein the training window or a particular dataset is shownin Figure , a plot o the training errors and test errors.During training the weights and biases o the network are

iteratively adjusted to minimize the network perormanceunction. In this example, the result is reasonable because o the ollowing considerations. Te nal mean-square error issmall. Te train and test set error have similar characteristics.No signicant overtting has occurred by iteration (wherethe best validation perormance occurs).

Regression analysis is a technique where the output willtrack the input and the value o . is a reasonable valueas shown in Figure . Also the complete process is donewith reduced time complexity, since a computer tomography image with hundreds o slices is used; the parameter men-tioned in Figures and is much needed. Tus, the uidpackets are identied and extracted by the proposed neural

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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Best training performance is 0.065178 at epoch 2

M e a n s q u a r e d e r r o r ( m s e )

2 epochs

TrainTestBest

100

101

10−1

10−2

F : Perormance plot.

Divide the axial slice into × blocksor ( = 1,

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0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

Target

DataFit

Y = T

Training: R = 0.96662

O u t p u t ≅

0 . 9

1

∗

t a r g e t +

0 . 0

5 9

(a)

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

Target

DataFit

Y = T

Test: R = 0.96446

O u t p u t ≅

0 . 9

∗

t a r g e t +

0 . 0

6 2

(b)

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

Target

Data

Fit

Y = T

All: R = 0.96629

O u t p u t ≅

0 . 9

1

∗

t a r g e t +

0 . 0

5 9

(c)

F : Regression analysis plot.

3. Results

Te experimental results are carried out to evaluate theperormance o the proposed approach. Te proposed seg-mentation method is used or segmentation o colon andthis will serve as a basis or the identication o polypswhich will lie on the walls o the colon or the diagnosis

o colorectal cancer. For experiments, rom the total o datasets, datasets were downloaded rom CIA cancerimaging archive, in the supine position and in the proneposition. Te remaining datasets were real time datasetsobtained rom clarity imaging centre, Coimbatore. Tus themanual segmentation o dataset segmented using IKsnap is shown in Figure (a). Figure contains colon, small

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Initialize a new slice (Diff) to store the output by SLDR Assign = No o continuous segments in slice (

)

For ( = 1, 45

Select the organ in initial segmented slice (IS) based on the location predicted above and assign it to variable BW

I (the organ selected by BW matches with the organ in previous Segmented slice) and(area o that segmented portion >) //A conf ormation Step to av oid colon to mix up with non-colon seg mentsAssign BW = alse

Repeat the above process and concatenate the segments in that new slice by means o OR operation

A : Steps implemented or the removal o bowels.

(a) (b) (c)

F : (a) Input: sample axial slice. (b) Output: 0. (c) OutF = And(0, 1).

intestine, mixed (o colon andsmall intestine through openedileocecal valve which happens in low requency), stomachor lung, and noise. Tus the colon portions that are to besegmented by the proposed method are highlighted in redcolour and the bowels that are to be removed are highlightedin green colour. Te results o the automatic segmentationgenerated by our proposed method beore the removal o bowels are also shown in Figure (b). Tus the two guresshow similar characteristics.

An example o colon segmentation or a ew sample slicesortwo sample dataset is shown in able . Te sample dataset

consists o slices. ables (a), (b), and (c) show thenal result o our proposed method or ew sample slices, ,, and slices o the dataset . Tus the sample slices and shown in ables (b) and (c) consist o colon,

bowels, and uid region; thus the removal o the bowel romthe sample slice is highlighted by red colour in the category difference and the concatenation o the opacied uid withthe colon is shown in the output section.

Ten the output o sample axial slices and o dataset (numbero slices: ) is shown in ables (d) and (e). Te

D rendered outputs produced by the proposed segmentationmethod or sample three dataset are shown in Figure whenthere is absence o polyps.

I there is a polyp in the cecum then the segmentation o the colon will be incomplete as shown in Figure .

4. Discussion

Te true positive raction (PF), or sensitivity, the specicity

or alse positive ractions (FPF), and accuracy which is thedegree o closeness o measurements o a quantity to thatquantity’s actual-true value are also used or evaluation. Forthe evaluation o the above parameters, the number o colonsegments which are classied correctly (true positives, P)and misclassied as noncolon segments (alse positives), thenumber o noncolon segments which are classied correctly as noncolon (true negatives, N) and misclassied o colonsegments (alse negative, FN) should be calculated. In addi-tion to the above parameters, the error and dice coefficient(DC) is also computed. able depicts the average value o the PF, FPF, DC, accuracy, and error. Te equations thatwere used or the calculation o the parameters are as ollows.

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Tiny holes

(a) (b)

F : (a) OutF = And(0,

1). (b) Removal o tiny holes.

(a)

Partial volume effect (PVE)

Bowels

(b)

Colon

Boundaries of noncolonsegments

(c)

F : (a) Removal o PVE. (b) Removal o bowels. (c) Output o SLDR.

(a) (b)

F : (a) Manual segmentation o dataset which contains both bowels and colon. (b) Results o the automatic segmentation beore theremoval o bowels.

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T

:

O u t p u t o

f s a m p

l e a x

i a l s l

i c e s o

f d a t a s e t .

I n d e x

I n p u t i m a g e

0

G r o u n

d t r u t h

D i ff e r e n c e

O u t p u t

S a m p

l e s l

i c e n u m

b e r

O u

t p u t o

f s a m p

l e a x

i a l s l

i c e s o

f d a t a s e t ( n u m

b e r o

f s l

i c e s :

)

( a )

( b )

B o w e l s

( c )

B o w e l s

C o n c a t e n a t i o n o f

u i d w i t h c o l o n

O u

t p u t o

f s a m p

l e a x

i a l s l

i c e s o

f d a t a s e t ( n u m

b e r o

f s l

i c e s :

)

( d )

( e )

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(a) (b) (c)

F : (a) D rendered output o dataset . (b) D rendered output o dataset . (c) D rendered output o dataset .

F : Segmentation o the colon when there is a polyp in thececum.

.. Sensitivity. Te equation or the calculation o sensitivity is dened by

Sensitivity =

P

FN + P . ()

.. Specicity. Te equation or the calculation o specicity is dened by

Specicity = N

FP + N. ()

.. Dice Coefficient. o evaluate our method we comparedits segmentation by our proposed method (B) with manualsegmentations (gold standard) and computed the dice coe-cient (DC) as a perormance measure as specied in ().

I DC = means perect overlap between two segmentations[, ], then

DC = 2 |A ∩ B|

|A| + |B|. ()

.. Accuracy. Te equation or the calculation o accuracy isdened by

Accuracy = P + N

FP + FN + P + N. ()

Te results o our proposed method were reported in able and in able results were compared with those o two othersegmentations technique proposed or the segmentation o colon: adaptive level sets and graph cuts. In all the cases theresults o the algorithms are compared with those obtainedby the manual identication o the colon segments by radiologist.

able depicts the average value o the PF, FPF, DC,accuracy, and error. From the table, it can be said that theproposed method perorms better on several datasets interms o sensitivity, specicity, accuracy, and so orth.

Te algorithm correctly identied the large colon seg-ments. All the alse positive ractions were due to the small

bowel which lies very close to the colon and is indicatedin green color in Figure and able (b) in the category difference. Te size, position, and the occurrence o bowelswill decide the overall perormance o the proposed method.Te lungs segments which should be removed perectly by thelung removal stage also decide the parameter values since thelarge segments are easily removed and care has been takento remove even the small lung segments and still a raction o the small lung segments result in the lowering the parameters

value.Te accuracy o the graph cut approach is o a lower grade

compared to that o the other two techniques, even thoughit has the benet o nding global solution. Tis is unlike

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: List o parameters evaluated by the proposed method.

Number o patient Specicity (%) Sensitivity (%) Dice coefficient (measured over value ) Accuracy (%) Error (%)

datasets rom cancer imaging archive

well distended . .

collapsed .

real time datasets well distended . .

collapsed .

Average . . —

: abulation o comparison o the parameters.

Parameters Graph cuts [] Level sets [] Proposed method

Accuracy .% .% %

Sensitivity .% .% .%

Specicity .% .% %

the level sets approach which gets straightorwardly trappedin local minima, requiring manual seeds to be placed insideevery colon segment or the procient segmentation process.Although level sets provide high segmentation accuracy which is close to the proposed method, we highlight thatmanual seeding is time consuming and error-prone. Tusour proposed method does not stuck anywhere and even

very small colon segments which have been missed by theradiologist are correctly identied by our method.

Te results o the proposed method are reported inable ; results were compared with those o two othersegmentation techniques like adaptive level sets and graphcuts. In all the cases the results o the algorithm are compared

with those obtained by the manual identication o the colonsegments by radiologist. Te number o slices per dataset varies rom to depending on the height o the patient.

able shows the comparison results o existing and pro-posed method. Te proposed segmentation method achieves% o accuracy in colon segmentation, whereas the GraphCuts approach is .% and Level Sets is .%. Tis is due toefficient proposed approaches and their step by step process.Similarly, the sensitivity value o the existing Graph Cutsand Level Sets algorithm is o .% and .%, whereasthe proposed segmentation approach acquires .%. Speci-city (sometimes called the true negative rate) measuresthe proportion o negatives which are correctly identied,

in which the proposed approach specicity value is %,whereas the Graph Cuts approach is .% and Level Setsis %. Hence the proposed approach perorms better whencompared to other existing approaches.

5. Conclusion

Accurate and automatic colon segmentation is crucial ora virtual colonoscopy system. Te proposed approach was

validated on the data downloaded rom CIA cancer imagingarchive and on real data set. CIA cancer imaging archivehas a wide variety o collections o C colonography, inwhich the cases are ree o polyps. Te dataset that

has been used here is among the cases. Te proposedmethod will segment different portions o colon or eachslice using the steps which has been illustrated earlier andthe segmented portions were compared with the groundtruth images and the different parameters were calculated to

validate the proposed method. Tus the proposed automaticsegmentation method outperormed the other two methodsas the importance is given to the extraction o opacied uidand the removal o even small bowels that may lower theaccuracy value. We provided quantitative results based on datasets having challenges. Hence the proposed work obtainsaccurate results or a ully automated system, compared withother known segmentation algorithms.

Conflict of Interests

Te authors declare that there is no conict o interestsregarding the publication o this paper.

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