Browsing by Author "Kacmaz, Rukiye Nur"

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Automatic blurry colon image detection using laplacian operator-based features
(ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS, 2018) Yilmaz, Bulen; Kacmaz, Rukiye Nur; Dundar, Mehmet Sait; 0000-0002-0336-4825; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
Conference Conference: European Biotechnology Congress Location: Athens, GREECE Date: APR 26-28, 2018
Detection of Ulcerative Colitis From Colonoscopy Images
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Kacmaz, Rukiye Nur; Yilmaz, Bulent; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
Ulcerative colitis (UC) is a disease in which inner surface of colon is inflamed. Ulcers and open scars on the colon are observed. The complaint in the flare period is the frequent bloody diarrhea. Complaints of people with UC increase and decrease periodically. Colonoscopy is the most preferred approach for the visualization of the gastrointestinal tract for the diagnosis and follow-up of related diseases, and UC in particular. The lack of experience of the colonoscopist, complicated locality of the lesion, and the rush in the colonoscopy suite to complete the procedure as soon as possible may cause mistakes in visual analysis. In this study, 200 colonoscopy images (100 normal, 100 UC) were used. The statistical features such as gray level variance, gray level local variance, normalized variance, histogram range, and entropy were extracted from the images, and a normalized 200x5 feature matrix was formed. The normal images and images with UC were discriminated using support vector machines and k-nearest neighbors. It should be noted that the extraction of only 5 features from the colonoscopy images resulted in 95% accuracy. This study demonstrated the feasibility of the development of software tools for aiding the physicians in the diagnosis of colon diseases.
Detection of Variation Instances on Colonoscopy Videos using Structural Similarity Index
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Kacmaz, Rukiye Nur; Yilmaz, Bulent; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
The aim of this study is to reduce the number of images extracted from the videos recorded by the specialists during the colonoscopy process for further examination, thereby enabling the specialist to deal with fewer images. Since the images obtained from the videos are very similar, the main assumption of this study is that the whole video can be represented by fewer images. The approach used in this study is the structural similarity index. Totally, images were obtained from 4 different videos coming from healthy, ulcerative colitis, Crohn's, and polyp patients. The noisy images in these videos were eliminated manually. When the structural similarity index between two consecutive clear images was less than 0.83, the second image was selected and shown to the specialist for his/her examination. By this way, the frames carrying significantly new information from the videos were defined as the variation instances. The tests on healthy or diseased colon videos showed that only 5-10% of the clear images provide significantly new information.
Effect of Bilinear Interpolation on the Texture Analysis of Colonoscopy Images
(IEEE345 E 47TH ST, NEW YORK, NY 10017 USA, 2017) Kacmaz, Rukiye Nur; Yilmaz, Bulent; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
Interpolation is a method that is used to obtain unknown intensities with the help of known intensities on an image. This method is frequently used in the literature to eliminate light reflection on colonoscopy images. Texture features are the most important characteristics used to describe the region or objects of interest in the image. They are the measures of intensity variation of a surface that determine properties such as smoothness, roughness, and regularity. The aim of this study is to find out the how bilinear interpolation applied on colonoscopy images with reflection impact texture features obtained from the same images. A research carried out to make reasonable comparison between a texture feature from an image with no reflection and the same feature obtained from the same image with synthetically added reflections with various percentages. Using the approaches like gray level co-occurence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray tone difference matrix (NGTDM) 126 features were extracted from each 32x32 sub-images coming from 610 colonoscopy images. Several of the features extracted from sub-images with no reflection and reflection were not statistically significantly different, while majority of them were affected from the reflections.
Effect of interpolation on specular reflections in texture-based automatic colonic polyp detection
(WILEY, 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2020) Kacmaz, Rukiye Nur; Yilmaz, Bulent; Aydin, Zafer; 0000-0002-3237-9997; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
Reflections of LED light cause unwanted noise effects called specular reflection (SR) on colonoscopic images. The aim of this study was to seek answers to the following two questions. (a) How are the texture features used in automatic detection of polyps affected by the interpolation on specular reflections? (b) If they are affected does it really affect the classification performance? In order to answer these questions, we used 610 colonoscopy images, and divided each image into tiles whose sizes were 32-by-32 pixels. From these tiles, we selected the ones without any specular reflection. We added different shape and size specular reflections cropped from real images onto the reflection-free tiles. We then used the nearest neighbors, bilinear and bicubic interpolation techniques on the tiles on which SRs were added. On these tiles we extracted 116 texture features using 3 second-order approaches, and 4 first-order statistics. First, we used paired samplettest. Second, we performed automatic classification of polyps and background using random forest and k nearest neighbors (k-NN) approaches using the texture features for different combinations of specular reflections added on the tiles from the polyp or background. The results showed that depending on the size of specular reflection, interpolation can cause a significant difference between the texture features that were coming from reflection-free tiles and the same tiles on which interpolation was performed. In addition, we note that bicubic interpolation may be preferred to eliminate specular reflection when texture features are used for background and polyp discrimination.