Computer-Aided Diagnosis System for Detection of Stomach Cancer with Image Processing Techniques
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Stomach cancer is a type of cancer that is hard to detect at an early stage because it gives almost no symptoms at the beginning. Stomach cancer is an increasing incidence of cancer both in the World as well as in Turkey. The most common method used worldwide for gastric cancer diagnosis is endoscopy. However, definitive diagnosis is made with endoscopic biopsy results. Diagnosis with endoscopy is a very specific and sensitive method. With high-resolution endoscopy it is possible to detect mild discolorations, bulges and structural changes of the surface of the mucosa. However, because the procedures are performed with the eye of a doctor, it is possible that the cancerous areas may be missed and / or incompletely detected. Because of the fact that the cancerous area cannot be completely detected may cause the problem of cancer recurrence after a certain period of surgical intervention. In order to overcome this problem, a computerized decision support system (CDS) has been implemented with the help of specialist physicians and image processing techniques. The performed CDS system works as an assistant to doctors of gastroenterology, helping to identify the cancerous area in the endoscopic images of the scaffold, to take biopsies from these areas and to make a better diagnosis. We believe that gastric cancer will be helpful in determining the area and biopsy samples taken from the patient will be useful in determining the area. It is therefore considered a useful model.
KeywordsStomach cancer Region growing Statistical region merging Statistical region merging with region growing Segmentation Image processing Computerized decision support(CDS) system
This work was supported by the project number 15101020 by Selcuk University Scientific Research Projects Coordination Office (BAP).
Compliance with Ethical Standards
Ethics Approval and Consent to Participate
The whole study was approved by the local research ethics committee of Faculty of Medicine Affiliated to Selcuk University (Selçuklu, Konya Province, Turkey).
Conflicts of Interest
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
- 2.Alpert, M. A., Terry, B. E., Mulekar, M., Cohen, M. V., Massey, C. V., Fan, T. M. et al., Cardiac morphology and left ventricular function in normotensive morbidly obese patients with and without congestive heart failure, and effect of weight loss. Am. J. Cardiol. 80(6):736–740, 1997.CrossRefGoogle Scholar
- 4.Brenner, H., Rothenbacher, D., and Arndt, V., Epidemiology of stomach cancer. Cancer Epidemiol. Modifiable Factors. 467–477, 2009.Google Scholar
- 5.Prof. Dr. K Yalçın POLAT https://www.memorial.com.tr/saglik-rehberleri/mide-kanseri/ “How to diagnose and treat stomach cancer”, Last Access (03.08.2017).
- 6.Akbari, H., Kosugi, Y., Kojima, K., and Tanaka, N., Hyperspectral image segmentation and its application in abdominal surgery. Int. J. Funct. Inf. Pers. Med. 2(2):201–216, 2009.Google Scholar
- 8.Dandıl, E., Ekşi, Z., & Çakıroğlu, M., Mamogram Görüntülerinden Bilgisayar Destekli Kitle Teşhisi Sistemi.Google Scholar
- 9.Jayas, D. S., and Karunakaran, C., Machine vision system in postharvest tecnology. Stewart Postharvest Rewiev. 22, 2005.Google Scholar
- 12.Karhan, M., Oktay, M. O., Karhan, Z., & Demir, H.,. Morfolojik görüntü işleme yöntemleri ile kayısılarda yaprak delen (çil) hastalığı sonucu oluşan lekelerin tespiti. In 6th International Advanced Technologies Symposium (IATS’11) (pp 172–176), 2011.Google Scholar
- 13.Keefe, P. D., A dedicated wheat grain image analyzer. Plant Varieties Seeds 5:27–33, 1992.Google Scholar
- 14.Mookiah, M. R. K., Rajendra Acharya, U., Martis, R. J., Chua, C. K., Lim, C. M., Ng, E. Y. K., and Laude, A., Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach. Knowl.-Based Syst. 39:9–22, 2013.CrossRefGoogle Scholar
- 15.Pratta, H., Coenenb, F., Broadbentc, D.M., Hardinga, S.P., and Zhenga, Y., Convolutional neural networks for diabetic retinopathy. Procedia Comp Sci. 200–205, 2016.Google Scholar
- 18.Xuan, J., Adali, T., and Wang, Y., Segmentation of magnetic resonance brain image: integrating region growing and edge detection. Image Processing, 1995. Proceedings, International Conference on. Vol. 3. IEEE, 1995.Google Scholar
- 19.Ballard, D.H., Brown C.M., Computer Vision (pp 149), 1982.Google Scholar
- 20.Tang, J., A color image segmentation algorithm based on region growing. In Computer engineering and technology (iccet), 2010 2nd international conference on (Vol. 6, pp. V6-634). IEEE., (2010, April).Google Scholar
- 21.Yasar, A., Saritas, I., Korkmaz, H., The detection of stomach cancer with semi-automatic region growing segmentation method. Ciencia e Tecnica Vitivinicola Journal (ISSN: 0254-0223), 206–220, 2017.Google Scholar
- 22.Gonzalez, R. C., Woods, R. E., Digital image processing. Singapore: Pearson Education, 2014.Google Scholar
- 24.Celebi, M. E., Kingravi, H. A., Iyatomi, H., Lee, J., Aslandogan, Y. A., Van Stoecker, W., ... & Marghoob, A. A., Fast and accurate border detection in dermoscopy images using statistical region merging. SPIE., (2007, March).Google Scholar
- 26.Zhu, W., Zeng, N., and Wang, N., Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG proceedings: health care and life sciences, Baltimore, Maryland, 19, 2010.Google Scholar
- 27.Sokolova, M., Japkowicz, N., and Szpakowicz, S., Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In Australasian joint conference on artificial intelligence (pp 1015–1021). Springer, Berlin, Heidelberg, (2006, December).Google Scholar