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This paper presents a method to diagnose prostate cancer on multiparametric magnetic resonance imaging (Mp-MRI) using the shearlet transform. The objective is classification of benign and malignant regions on transverse relaxation time weighted (T2W), dynamic contrast enhanced (DCE), and apparent diffusion coefficient (ADC) images. Compared with conventional wavelet filters, shearlet has inherent...
In this paper we propose a method for feature representation and classification of prostate tissue images using the shearlet transform. The objective is to automatically process biopsy tissue images and assist pathologists in analysing carcinoma cells. Furthermore, we will automatically grade the cancer using the Gleason grading system. The main contribution of this paper is the use of a holistic...
This paper presents a method for representation and classification of microscopic tissue images using the shear let transform. The objective is to automatically process biopsy tissue images and assist pathologists in analyzing carcinoma cells, e.g. differentiating between benign and malignant cells in breast tissues. Compared with wavelet filters such as the Gabor filter, shear let has inherent directional...
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