Ms. Praniti GolharDepartment of Computer Engineering,MIT College of Engineering, PunePune, 411038, Maharashtra, India.Prof. Dr. Siddhivinayak KulkarniDepartment of Computer Engineering,MIT College of Engineering, PunePune, 411038, Maharashtra, India.
Abstract — in recent years, Ulcerative Colitis (UC) is most common and severe type of cancer and to detect its stages and severity is most challenging job. Ulcerative Colitis (UC) is a common intestinal complication which causes polyps in the rectum which may develop further into cancer. UC causes deaths of about half a million people every year.
Colonoscopy images are obtained by process called colonoscopy. There are various shapes of polyps at various stages. Detection of stages of UC is impossible by naked eyes. In an optical colonoscopy course, the endoscopist looks for colon polyps. Hyperplastic polyp is benign lesion; adenomatous polyp is likely to become cancerous. Hence, it is common practice to remove all of the identified polyps and send them to subsequent histological analysis. So we are proposing a technique in which detection of infectious area can be done with help of medical image processing.
So location of infectious area, blood clotting can be detected with help of color impact. Shape and texture detection is used for checking type of polyp. The technique can be useful in designing the treatment of UC and will help in prevention of disease.Keywords: colorectal cancer, Ulcerative Colitis, Image processing, Colonoscopy, Narrow Band Image.I.
INTRODUCTION1.1 Medical Image ProcessingIn medical image processing, it is a process where images from interior of body get scanned, selected and then get analyzed to check internal complications of human body. To do this analysis, a database is formed which will be under analysis and then use to identify abnormalities.Here, original data will be remaining as it is during analysis.
Medical images will be great advantage for physicians to search the abnormalities in image very quickly.1.2 Ulcerative ColitisUlcerative Colitis is an inflammatory bowel disease, which causes inflammation in ulcer, digestive track.
Ulcerative Colitis leads to many deaths in world as well as in India which is more than a million per year. Treatment can comfort, but this condition can’t be healed. For detecting stage of disease, there is requirement for a medical diagnosis Lab tests or imaging always required Chronic. Ulcerative colitis is detected in innermost lining of the large intestine (colon) and rectum. There are various stages from mild to severe. Because of ulcerative colitis, chances of having colon cancer will increase a lot.
Symptoms for this disease are rectal bleeding, bloody diarrhea, abdominal cramps and pain. Treatment includes medication and surgery.Figure 1. Colonoscopy Images.The disease has most important 4 stages and the last of them leads to cancer. The affected abnormal part has different textures in various stages. Since these patterns are difficult to detect with naked eyes by the doctor, PIT pattern and Texture Recognition techniques are used.
The most important algorithms used are related to various feature extraction techniques.Figure 2. Ages affected in UC.1.
3 ColonoscopyColonoscopy is technique where CCD camera or a fiber optic camera on a flexible tube passed through the anus. With helpof this doctor can suspected colorectal cancer lesions, polyps smaller than 1 mm. Once polyps are removed, microscope can determine if they are cancerous or not. It is very long process to make polyp cancerous which may be of span of 15 years.Figure 3.
ColonoscopyThe colonoscopy is performed by a doctor experienced in the procedure and lasts approximately 30-60 minutes. Medications will be given into your vein to make you feel relaxed and drowsy. You will be asked to lie on your left side on the examining table. During a colonoscopy, the doctor uses a colonoscope, a long, flexible, tubular instrument about 1/2-inch in diameter that transmits an image of the lining of the colon so the doctor can examine it for any abnormalities.2.4 Stages of Ulcerative ColitisPit pattern: Pit pattern is useful in checking stage of disease with help of colonoscopy, from where severity can be checked.
So it is ideal to detect stage during colonoscopy to make process very fast.Figure 4. Pit Pattern2.5 Importance of the Research? Development of polyp to cancer is very long, 10 to 15 year.? Chances of cancer to spread all over body and then chances for life will be decrease.
? Risk factors – overweight or obese, physically inactive, diet, Smoking, heavy alcohol use? Colorectal cancer risk factors you cannot change : Being older, personal and family history, Having type 2 diabetes? Factors with unclear effects on colorectal cancer risk: Night shift work, previous treatment for certain cancers.II. LITERATURE REVIEWColorectal cancer is largest death causing disease in world. So to analyzing this disease medical treatment which is suggested is colonoscopy 15. Kudo et al.
5 had discussed about patterns which are being produced by colonoscopy results. They divided pit patterns into seven principal types: (1) normal round pit; (2) small round pit; (3) small asteroid pit; (4) large asteroid pit; (5) oval pit; (6) gyrus-like pit; and (7) non-pit. So further distinguish done by authors 9 16 14 on this Kudo’s pit patterns.Pablo Mesejo et al. 9 discuss about classification of colonoscopy videos are classified in 3 classes as Adenoma, Hyperplastic and serrated adenoma.
Here machine learning algorithms are used; it will help clinicians in virtual biopsy of hyperplastic, serrated adenoma and adenoma. So technique is developed for diagnosis gastrointestinal lesions from regular colonoscopy. So by using this technique systematic biopsy for suspected hyperplastic tissues also 3D shape features improves classification accuracy.Also many techniques analyses about NBI images 16 3 7 17 for classification. Toru Tamaki et al. 16 divides NBI images into 3 classes as Type A, B, C3. For classification local recognition method as Bag-of-Words is introduced along with SVM classifier. Local features are considered and checks result for recognition checking.
Hao Chun Wang et al. 3 classifies according to Classification of Regional Feature (CoRF) which is extension of sparse matrix and vector quantization for feature detection and segmentation. Mineo Iwatate et al. 7 discuss about efficiency and magnifying colonoscopy with NBI image to detect, histological predict and estimation of depth of early rectal cancer. Here, NBI International Colorectal Endoscopic (NICE) classification is introduced here. Yasushi Sano et al.
17 discusses about the work by Japan NBI Expert Team (JNET) where they discussed about Sano, Hiroshima, Showa, And Jike Classifications Based on The Findings of NBI Magnifying Endoscopy. Also they discussed about Universal NBI Magnifying Endoscopic Classification of Colorectal Tumors: Japan NBI Expert Team (JNET) Classification which is universal solution which has overcome the problems rose by previous methods. The JNET classification combining previous classifications to give common diagnostic criteria to promote academic progress of NBI.For detection of polyp from colonoscopy 4 14 various techniques are introduced. Ju Lynn Ong et al. 4 gives idea about features of image like geometric feature, colon wall extraction construction of probability density functions(PDF), comparison of shape distribution. Here K-L divergence used for comparison between PDF for specific image and previous database. Yuan Shen and Christopher L.
Wyatt 14 uses feature extraction method for Computer Aided polyp Detection (CAPD) on basis of principal curvature, Gaussian curvature and mean curvature, shape index, curvedness,maximum and minimum polyp radius etc. Principle Component Analysis is used with wrapper method.For feature extraction of images various techniques 1 2 6 13 used for extracting features. Adegoke, B.
O. et al. 1 surveyed about medical image feature extraction. They researched about CBIR (Content- Based Image Retrieval). The algorithms used in these systems are commonly divided into three types as Extraction, Selection and Classification. Different available medical image feature extraction had been studied in this paper.
G.Nagarajan et al.2 proposes minimum description length principle based genetic algorithm (GA) approach for the selection of dimensionality reduced set of features.
There are 3 phases are developed as for the extraction of the features are Texton based contour gradient extraction algorithm, Intrinsic pattern extraction algorithm and modified shift invariant feature transformation algorithm. For second phase to identify the potential feature vector GA based feature selection is done, with help of “Branch and Bound Algorithm” and “Artificial Bee Colony Algorithm”. To improve the presentation of the hybrid content based medical image retrieval system, feedback method is implemented. For this algorithm they used techniques such as Intrinsic pattern extraction algorithm using PCA.
The branch and bound algorithm is used to give reduced feature vector. M.VASANTHA et al. 6 researched about breast cancer where proposes an image classifier to classify the mammogram images. For preprocessing they used low pass filter to remove noise. In this Work intensity histogram features and Gray Level Co-Occurrence Matrix(GLCM) features are Extracted.
For classification we used J48 classifier, a decision tree classifier based on C4.5, from WEKA to train and test the features. Seyyid Ahmed Medjahed et al.13 have done a comparative Study of Feature Extraction Methods in Images Classification, in which they had discussed about Feature Extraction Techniques and classifiers on the Cal-tech 101 image dataset. classification accuracy rate, Precision, Recall, Measure, G_mean, AUC and the Roc Curve are used to check the performance.Similarly some techniques have been developed for x-ray results1012 which also helpful for feature extraction. Randa Hassan Ziedan et al.
10 proposes a technique for classification of x-ray images, where they discussed about feature extraction techniques GLCM, LBP, Canny and BoW. Also a comparative study for these techniques for x-ray images. Seyyed Mohammad Mohammadi et al.12 also proposed shaped texture feature extraction for x-ray images. In research Novel Shape Texture feature is proposed with help of histogram adjustment, Noise removal, Edge and boundary extraction, phase congruency computation, Gabor Transformation, shape-texture feature extraction with help of classifiers as Euclidean Distance, PNN( probabilistic Neural network) and SVM.Saima Rathore et al.
11 discusses about colon cancer detection techniques as region based segmentation methodologies, classification and segmentation, graph based techniques, automated diagnosis system etc. Similarly Shiva K Ratuapli et al.15 gives idea about post colonoscopy follow-up with help of previous screening and surveillance colonoscopy. Mohammad Sohrabinia et al. 8 uses different image analysis and processing methods in order to extract information content needed to update large scale maps.2.1 Problem StatementTo detect state/ stage of lesion, also image related more information detection as by colour analysis (redness) or pattern formation such type of data reporting generation are major goals.To make a system to detect actual pattern or shape of lesion from image also stage of Ulcerative Colitis disease.
III.PROPOSED APPROACH3.1 Proposed MethodFigure 5.
Proposed System ArchitectureIn this project, we are proposing a method for polyp detection.1) Red color filtering –? Red colour filtering will be used, which will help to detect maximum swelling or blood clotting area. This swelling and blood clotting is considered as initial phase of Ulcerative Colitis.
? It can be considered by clinicians as most infected area.2) RoI selection –? From loaded image, Region of Interest is selected.3) Shape/Texture detection –? Edge detection and shape detection is done.
? For edge detection, we have analyzed canny edge detector and sobel edge detection which will give edge of RoI.4) Classifier –? Classifier will be useful in classifying Shape/Texture according to classes.5) Final result –? Final result will be combination of red colour filtering and RoI analysis.2.2 Algorithms3.2.1 Canny Edge DetectorThe algorithm runs in 5 separate steps:1. Smoothing: Blurring of the image to remove noise.
2. Finding gradients: The edges should be marked where the gradients of the image has large magnitudes.3. Non-maximum suppression: Here edges which are detected having gradient value are suppressed to sharp edges, but local maxima are exception.4. Double thresholding: Potential edges are determined by thresholding.
5. Edge tracking by hysteresis: Final edges are determined by suppressing all edges that are not connected to a very certain (strong) edge.3.2.2 Sobel Edge DetectorFigure 6. Sobel Edge Detector3.
3.3 Red colour filteringFigure 7. Red Colour Filtering3.4 ImplementationsFigure 9 is taken as sample test case image on which all mentioned algorithms which will be applied.Figure 8. Test imageA. Red colour filteringAlgorithm 4.
3.3 is implemented on test image (Figure 8), which is considered as Stage 1. And result of applying Red Colour Filtering algorithm is shown as below.
Figure 9. Red Colour FilteringB. Edge DetectionAlgorithm 4.3.
1 and 4.3.2 are implemented on test image (Figure 8), which is considered as part of Stage 2. And result of applying Canny edge Detector and Sobel Edge Detector algorithm is shown as below in Figure 10 and 11 respectively.a. Canny Edge DetectorFigure 10. Canny edge detectionb.
Sobel Edge DetectorFigure 11. Sobel Edge DetectionV.CONCLUSIONWith help of this project, we are trying to take advantage and make a project for a social cause which will surely help doctors with less expertise and in remote areas to detect the stage and the probable infections in the colon image. It will help doctors to detect the abnormalities which are not able to be seen by the naked eye.The proposed techniques will surely help doctor to get faster result so that time which is spent on post colonoscopy will be avoided.
Also, in rural area or unprivileged area where lack of technology is challenge then this technology can give idea about disease severeness.ACKNOWLEDGMENTPraniti Golhar would like to thank Prof. Dr. Siddhivinayak Kulkarni for his guidance, Prof.
Deepali Jawale for her help in this work. We would like to thank all teaching and non-teaching staff for their support. We would like to take this opportunity to thank Head of Department Dr.Bharati Dixit and Principal Dr.
Anil Hiwale. We would also like to thank the institute for providing the required facilities, Internet access and important books. We are thankful to the authorities of Savitribai Phule University, Pune and concern members of cPGCON2017 conference, organized by, for their constant guidelines and support.
We are also thankful to the reviewer for their valuable suggestions.REFERENCES1 Adegoke, B.O, Ola, B.O.
and Omotayo, M.E, “Review of Feature Selection Methods in Medical Image Processing”, IOSR Journal of Engineering (IOSRJEN), Vol. 04, Issue 01 (January.
2014), ||V4|| PP 01-052 G.Nagarajan, R.I.Minu, B Muthukumar, V.Vedanarayanan & S.
D.Sundarsingh, “Hybrid Genetic Algorithm for Medical Image Feature Extraction and selection”, In Procedia Computer Science 85 ( 2016 ), 455 – 4623 Hao Chun Wang, Wei Ming Chen, Yen Pin Lin, Wei Chih Shen, “Tumor Detecting in Colonoscopic Narrow-Band Imaging Data”, In 2012 IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2012) November 4-7, 2012, 564 – 568.4 Ju Lynn Ong, Abd-Krim Seghouane, Kevin Osborn,” Polyp Detection In Ct Colonography Based On Shape CharacteristicsAnd Kullback-Leibler Divergence”, 978-1-4244-2003-2/08/$25.
00 ©2008 IEEE5 Kudo, S., Hirota, S., Nakajima, T., Hosobe, S., Kusaka, H., Kobayashi, T.
, Himori, M., Yagyuu, A., 1994, “Colourectal tumours and pit pattern”. In Journal of Clinical Pathology 47, 880–885.6 M.VASANTHA, DR.
V.SUBBIAH BHARATHI, R.DHAMODHARAN, “Medical Image Feature, Extraction, Selection And Classification”, International Journal of Engineering Science and Technology, Vol. 2(6), 2010, 2071-20767 Mineo Iwatate, Taro Ikumoto, Santa Hattori, Wataru Sano, Yasushi Sano, and Takahiro Fujimori, “NBI and NBI Combined with Magnifying Colonoscopy”, In Hindawi Publishing Corporation, Diagnostic and Therapeutic Endoscopy, Volume 2012, Article ID 173269, 11 pages, doi:10.1155/2012/1732698 Mohammad Sohrabinia, Saeid Sadeghian, Dadfar Manavi, “Application of Image Processing and Image Analysis Methods ForLarge Scale Map Revision”, In the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol.
XXXVII. Part B4. Beijing 2008, 1351-1354.9 Pablo Mesejo, Daniel Pizarro, Armand Abergel, Olivier Rouquette, Sylvain Beorchia, et al.”Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy”.
In IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2016, 35(9), pp.2051 – 2063.10 Randa Hassan Ziedan, Mohamed A. Mead, Ghada S. Eltawel, ” Selecting the Appropriate Feature Extraction Techniques for Automatic Medical Images Classification”, In International Journal of Emerging Engineering Research and Technology Volume 4, Issue 5, May 2016, PP 1-911 Saima Rathore, Mutawarra Hussain, Ahmad Ali, Asifullah Khan, “A Recent Survey on Colon Cancer Detection Techniques”, ieeexplore.ieee.
org/iel7/8857/4359833/06565986.pdf12 Seyyed Mohammad Mohammadi, Mohammad Sadegh Helfroush, Kamran kazemi,”Novel Shape Texture Feature Extraction for medical X-Ray image classification” , In International journal of Innovative Computing, Information and Control, Volume 8, Number 1(B), January 2012, pp 659-67613 Seyyid Ahmed Medjahed, “A Comparative Study of Feature Extraction Methods in Images Classification”, In I.J. Image, Graphics and Signal Processing, 2015, 3, 16-2314 Shen, Y.
and Wyatt, C. L., “Open implementation of feature extraction methods for computer aided polyp detection with principal component analysis,” in 11th International Conference on Medical ImageComputing and Computer Assisted Intervention, 141-147 (2008).15 Shiva K. Ratuapli, Suryakanth R. Gurudu, Mary A.
Atia,Michael D. Crowell, Sarah B. Umar, M. Edwyn Harrison, Jonathan A. Leighton, and Francisco C.
Ramirez, “Post colonoscopy Follow-up Recommendations: Comparison with and without Use of Polyp Pathology”, In Hindawi Publishing Corporation, Diagnostic and erapeutic Endoscopy, Volume 2014, Article ID 683491, 7 pages, http://dx.doi.org/10.1155/2014/68349116 Toru Tamaki, Junki Yoshimuta, Misato Kawakami, Bisser Raytchev, Kazufumi Kaneda, Shigeto Yoshida, Yoshito Takemura, Keiichi Onji, Rie Miyaki, Shinji Tanaka, “Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using Local Features”, In Medical Image Analysis, December 17, 201317 Yasushi Sano, Shinji Tanaka, Shin-ei Kudo, Shoichi Saito, Takahisa Matsuda, Yoshiki Wada, Takahiro Fujii, Hiroaki Ikematsu, Toshio Uraoka, Nozomu Kobayashi, Hisashi Nakamura, Kinichi Hotta, Takahiro Horimatsu, Naoto Sakamoto, Kuang-I Fu, Osamu Tsuruta, Hiroshi Kawano, Hiroshi Kashida, Yoji Takeuchi, Hirohisa Machida, Toshihiro Kusaka, Naohisa Yoshida, Ichiro Hirata, Takeshi Terai, Hiro-o Yamano, Kazuhiro Kaneko, Takeshi Nakajima, Taku Sakamoto, Yuichiro Yamaguchi, Naoto Tamai, Naoko Nakano, Nana Hayashi, Shiro Oka, Mineo Iwatate, Hideki Ishikawa,Yoshitaka Murakami, Shigeaki Yoshida and Yutaka Saito, “Narrow-band imaging (NBI) magnifying endoscopic classification of colorectal tumors proposed by the Japan NBI Expert Team”, Digestive Endoscopy 2016; 28: 526–53318 https://www.gstatic.com/healthricherkp/pdf/ulcerative_colitis_en_IN.pdf19 https://www.webmd.com/colorectal-cancer/colonoscopy-what-you-need-to-know#1