Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
4 results
Search Results
Conference Object Citation - WoS: 11Citation - Scopus: 20ROI Detection in Mammogram Images Using Wavelet-Based Haralick and Hog Features(IEEE, 2018-12) Tasdemir, Sena Busra Yengec; Tasdemir, Kasim; Aydin, Zafer; Yengec Tasdemir, Sena BusraDigital mammography is a widespread medical imaging technique that is used for early detection and diagnosis of breast cancer. Detecting the region of interest (ROI) helps to locate the abnormal areas, which may be analyzed further by a radiologist or a CAD system. In this paper, a new classification method is proposed for ROI detection in mammography images. Features are extracted using Wavelet transform, Haralick and HOG descriptors. To reduce the number of dimensions and eliminate irrelevant features, a wrapper-based feature selection method is implemented. Several feature extraction methods and machine learning classifiers are compared by performing a leave-one-image-out cross-validation experiment on a difficult dataset. The proposed feature extraction method provides the best accuracy of 87.5% and the second-best area under curve (AUC) score of 84% when employed in a random forest classifier.Conference Object Citation - Scopus: 1PCB Component Recognition With Semi-Supervised Image Clustering(IEEE, 2021-06-09) Unal, Ahmet Emin; Tasdemir, Kasim; Bahcebasi, AkifClassification of surface mounted devices plays an important role on automated inspection systems of printed component board production. Limited number of publicly available datasets which the components are labeled and high intraclass variance in these datasets causes the supervised approches to be inefficient. In this study a deep learning method, enhanced with an unsupervised clustering system, which uses a small set of labeled data is proposed. The method compared with the current studies and the supervised systems. Most optimized setting reached high accuracy results by outrunning current classification methods.Conference Object Citation - Scopus: 3Mixture of Learners for Cancer Stem Cell Detection Using Cd13 and H&E Stained Images(SPIE - The International Society for Optics and Photonics, 2016-03-23) Oguz, Oguzhan; Akbas, Cem Emre; Mallah, Maen; Tasdemir, Kasim; Guzelcan, Ece Akhan; Muenzenmayer, Christian; Atalay, Rengul Cetin; Akhan Güzelcan, Ece; Tagdemir, KaslmIn this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H&E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and eigen-analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H&E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using HSLE stained microscopic tissue images.Conference Object MRD Biyoçip İçin Görüntü İşleme Temelli Sinyal Elde Etme Metodu(IEEE, 2019-04) Uslu, Fatma; Icoz, Kutay; Tasdemir, KasimThe response of the cancer patients to chemotherapy treatment varies from person to person. For some patients cancer cells are resistant to treatment and these cells can relapse again which is known as minimal residual disease. A microfluidic-based biochip capable of monitoring minimal residual disease is under development by our research group. The role of the biochip is to capture the target cells, which were separated by immunomagnetic beads on micro square tiles. Then biochips are imaged using a bright field optical microscope and it is planned to perform image-processing methods to detect the target cells, immunomagnetic beads and micro tiles. In this work the current progress of image processing methods for differentiating the immunomagnetic beads and micro tiles is presented.