Mammographic lesion classification using discrete orthonormal s-transform

Breast cancer is the leading cause of cancer in women. Early detection of breast cancer through periodic screening improves the chances of recovery. However, the small and subtle signs of the early disease make the task of accurate diagnosis particularly arduous for radiologists. Computer aided diagnosis of the mammographic images is currently very popular as it helps radiologists classify lesions as normal or abnormal, benign or malignant. This thesis presents an efficient mammographic lesion classification approach for the detection of breast cancer. The approach uses the two dimensional discrete orthonormal S-transform (DOST) method to extract the coefficients from the digital mammograms. A feature selection algorithm based on statistical two-sample t-test method is used for the selection of significant coefficients from the high dimensional DOST coefficients. The selected significant coefficients are used as features for the classification of mammographic lesions as benign or malignant. This scheme utilizes a back propagation neural network as the classifier. The scheme is validated using MIAS database. The result shows an optimal classification accuracy rate of $97.4\%$ and a performance index value of AUC = $0.97$ in receiver operating characteristic (ROC) curve. These results are very promising in comparison with existing discrete wavelet transform (DWT)

Mammographical mass detection and classification using Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT) hybrid scheme

The purpose of this study is to implement accurate methods of detection and classification of benign and malignant breast masses in mammograms. Our new proposed method, which can be used as a diagnostic tool, is denoted Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT), and consists of four steps. The first step is homomorphic filtering for enhancement, and the second is detection of the region of interests (ROIs) using a Local Seed Region Growing (LSRG) algorithm, which we developed. The third step incoporates Spherical Wavelet Transform (SWT) and feature extraction. Finally the fourth step is classification, which consists of two sequential components: the 1st classification distinguishes the ROIs as either mass or non-mass and the 2nd classification distinguishes the masses as either benign or malignant using a Support Vector Machine (SVM). The mammograms used in this study were acquired from the hospital of Istanbul University (I.U.) in Turkey and the Mammographic Image Analysis Society (MIAS). The results demonstrate that the proposed scheme LSRG-SWT achieves 96% and 93.59% accuracy in mass/non-mass classification (1st component) and benign/malignant classification (2nd component) respectively when using the I.U. database with k-fold cross validation. The system achieves 94% and 91.67% accuracy in mass/non-mass classification and benign/malignant classification respectively when using the I.U. database as a training set and the MIAS database as a test set with external validation. (C) 2013 Elsevier Ltd. All rights reserved

A Hybrid Model for Segmentation of Images Generated by X-Ray Computed Tomography

Kompjuterizovana tomografija (CT) je u poslednje vreme ušla na velika vrata sa razvojem industrijskih CT sistema, usled njene primene u različitim oblastima, a uveliko ulazi i u polje koodinatne metrologije. Zbog karakterizacije objekata sačinjenih od različitih materijala (najčešće metala i plastike), javljaju se određeni problem u vidu nastanka artefakata kod rezultata dimenzionalnih merenja. Istraživanja koja su sprovedena u ovoj doktorskoj disertaciji se bave problemom redukcije uticaja tih artefakata i segmentacije 2D snimaka. Razvijen je novi model koji je baziran na primeni hibridne metode gde je izvršena kombinacija dve metode za obradu slike, a to su fazi klasterizacija i rast regiona. Aksenat je stavljen na primeni ove hibridne metode radi dobijanja tačnijih rezultata segmentacije, što direktno utiče i na rekonstrukciju dimenzionalno tačnijih 3D modela.Computed tomography (CT) has recently entered a large door with the development of industrial CT systems, due to its application in many different areas, and it is already entering the field of coordinate metrology. Due to its ability to non-destructively characterize objects made of different materials (typicaly metals and plastics), a certain problem arises in the form of artefacts that are present in the results. Research carried out in this dissertation deals with the problem of reducing the impact of these artefacts and the 2D image segmentation. A new model was developed based on the application of the hybrid method where a combination of two methods for image processing was performed, which are fuzzy clustering and region growing. The accent is emphasized in the application of this hybrid method in order to obtain more accurate segmentation results, which directly affects the reconstruction of dimensionally more accurate 3D models