CS 229 Project Milestone Report: Automated Segmentation of Breast Density

Diagnosis of brain tumor using PNN neural networks

Cells grow and then need a very neat method to create new cells that work properly to maintain the health of the body. When the ability to control the growth of the cells is lost, they are unconsidered and often divided without order. Exemplified cells form a tissue mass called the tumor. In fact, brain tumors are abnormal and uncontrolled cell proliferations. Segmentation methods are used in b...

CS 229 = = Final Project Report SPEECH & NOISE SEPARATION

In this course project I investigated machine learning approaches on separating speech signals from background noise. Keywords—MFCC, SVM, noise separation, source separation, spectrogram

Segmentation of the whole breast from low-dose chest CT images

The segmentation of whole breast serves as the first step towards automated breast lesion detection. It is also necessary for automatically assessing the breast density, which is considered to be an important risk factor for breast cancer. In this paper we present a fully automated algorithm to segment the whole breast in low-dose chest CT images (LDCT), which has been recommended as an annual ...

Automatic Segmentation of the Dense Tissue in Digital Mammograms for BIRADS Density Categorization

Currently, the Breast Imaging Reporting and Data System (BIRADS) density categorization is the most popular tool for density assessment among radiologists. However, it is subject to interobserver variabilities. Therefore, different automated methods have been proposed for dense tissue segmentation. In [1], a technique based on modeling of breast tissue using a Gaussian mixture model was propose...

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