SPARSE REPRESENTATION-BASED CLASSIFICATION FOR TUBERCULOSIS MICROSCOPIC IMAGES

  • 李 承諺

Student thesis: Master's Thesis

Abstract

In this thesis a new image analysis system has been proposed to classify and identify the mycobacterium tuberculosis from the microscopic images We apply Sparse Representation-based Classification (SRC) to identify the features extracted from image candidate regions and the images are obtained by an auto-focusing system After the computation is done the results of the classifications will be saved so that the medical technicians can review and check them later There are two stages for tuberculosis identification: detection and identification of the mycobacterium tuberculosis In the detection of mycobacterium tuberculosis there are four processing steps: image categorization color normalization detection of the candidate regions and extracting features By using the standard deviation of brightness and the color saturation images can be divided into three groups The image color parameter training and color normalization are performed individually for each group The purpose is to make the color distribution of the images become more consistent It would be beneficial for the subsequent processing For the detection of the candidate regions we use Linear Discriminant Analysis (LDA) to do color segmentation After labeling and applying the morphology processing candidate regions can be found from which we can extract the features In the identification part we classify the results of candidate regions into three cases: Tuberculosis (TB) Suspected-Tuberculosis (STB) and Non-Tuberculosis (NTB) According to the rule of SRC it is needed to construct a dictionary for each image group The types of the images in each hospital are different To solve the problem that the fixed dictionaries can only adapt to some types of the images we construct the parent dictionaries which are built by three sub-dictionaries of TB STB and NTB These sub-dictionaries are trained by K-SVD These parent dictionaries are beneficial to adapt to different types of images Finally we use the extracted features of input images and parent dictionaries to compute the sparse coefficients And then the errors of the representation results are computed with input data based on sub-dictionaries the identification results can be found according to these errors The sensitivity of mycobacterium tuberculosis identification is 95% and the specificity is 94 26%; these results are similar to the results of previous system However in the previous automatic mycobacterium tuberculosis identification system the time for training the classifiers is about one week but it takes only three days for our new system we almost conserve half of the time
Date of Award2015 Aug 25
Original languageEnglish
SupervisorYung-Nien Sun (Supervisor)

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