Texture based analysis and classification of lesions in medical images
Malu G
Texture based analysis and classification of lesions in medical images Malu G - PHD Thesis 2016
The aim of this work is to develop an automated lesion detection and diagnosis system for breast Dynamic Contrast Enhanced - Magnetic Resonance Imaging (DCE-MRI). Early detection and diagnosis reduce the high death rate of patients due to breast cancer. Mammography and MRI are the popular medical imaging modalities that are used for the detection of malignant lesions in breast images; both have advantages and disadvantages. MRI is nowadays, shown to be a promising adjunctive tool for the malignancy detection. An automated system for an easy and accurate diagnosis of breast cancer is the need of the hour.
In this study a number of algorithms, methods and techniques in image processing, mathematical, geometrical, and statistical equations, data mining, machine learning, and biological techniques were considered. Most appropriate techniques were used for the topic and were fortunate to conduct the experiments with real data obtained from Regional Cancer Centre, which was validated with the help of two eminent and experienced radiologists of the centre.
The study was focused on two major factors that exhibited malignancy property - Enhancement and Structural. The enhancement property was studied using kinetic features and the structural property was performed using shape and margin characteristics of lesion. Since the aim of the work was to develop a fully automated lesion detection and diagnosis system, the steps involved in the development was bit extensive. Different techniques, experimentations and evaluations required to malignancy detection were used. It included pre-processing, image registration, segmentation; region of interest identification (ROI), developing new shape and margin descriptors, deriving features from the circular mesh based labelling technique, feature extraction, feature selection and classification.
MEDICAL IMAGE PROCESSING
BREAST CANCER
LESION DETECTION
Texture based analysis and classification of lesions in medical images Malu G - PHD Thesis 2016
The aim of this work is to develop an automated lesion detection and diagnosis system for breast Dynamic Contrast Enhanced - Magnetic Resonance Imaging (DCE-MRI). Early detection and diagnosis reduce the high death rate of patients due to breast cancer. Mammography and MRI are the popular medical imaging modalities that are used for the detection of malignant lesions in breast images; both have advantages and disadvantages. MRI is nowadays, shown to be a promising adjunctive tool for the malignancy detection. An automated system for an easy and accurate diagnosis of breast cancer is the need of the hour.
In this study a number of algorithms, methods and techniques in image processing, mathematical, geometrical, and statistical equations, data mining, machine learning, and biological techniques were considered. Most appropriate techniques were used for the topic and were fortunate to conduct the experiments with real data obtained from Regional Cancer Centre, which was validated with the help of two eminent and experienced radiologists of the centre.
The study was focused on two major factors that exhibited malignancy property - Enhancement and Structural. The enhancement property was studied using kinetic features and the structural property was performed using shape and margin characteristics of lesion. Since the aim of the work was to develop a fully automated lesion detection and diagnosis system, the steps involved in the development was bit extensive. Different techniques, experimentations and evaluations required to malignancy detection were used. It included pre-processing, image registration, segmentation; region of interest identification (ROI), developing new shape and margin descriptors, deriving features from the circular mesh based labelling technique, feature extraction, feature selection and classification.
MEDICAL IMAGE PROCESSING
BREAST CANCER
LESION DETECTION