Document image processing using irregular pyramid structure

Ph.DDOCTOR OF PHILOSOPH

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Artificial neural network and its applications in quality process control, document recognition and biomedical imaging

In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require complicated image processing techniques and higher computational costs. But in real-time application, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable number of gray levels, usually two levels (binary image), and at the same time retain all necessary features of the original image or develop a complicated technique. A binary image can be obtained by thresholding the original image into two levels. Therefore, thresholding of a given image into binary image is a necessary step for most image analysis and recognition techniques. In this thesis, we have studied the effectiveness of using artificial neural network (ANN) in pharmaceutical, document recognition and biomedical imaging applications for image thresholding and classification purposes. Finally, we have developed edge-based, ANN-based and region-growing based image thresholding techniques to extract low contrast objects of interest and classify them into respective classes in those applications. Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue from the point of view of industry\u27s productivity and competitiveness. Computer vision-based automatic quality inspection and controller system is one of the solutions to this problem. Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In this thesis, we have developed an image processing system using edge-based image thresholding techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applications to pass the accepted and rejected capsules. In document recognition application, success of OCR mostly depends on the quality of the thresholded image. Non-uniform illumination, low contrast and complex background make it challenging in this application. In this thesis, optimal parameters for ANN-based local thresholding approach for gray scale composite document image with non-uniform background is proposed. An exhaustive search was conducted to select the optimal features and found that pixel value, mean and entropy are the most significant features at window size 3x3 in this application. For other applications, it might be different, but the procedure to find the optimal parameters is same. The average recognition rate 99.25% shows that the proposed 3 features at window size 3x3 are optimal in terms of recognition rate and PSNR compare to the ANN-based thresholding technique with different parameters presented in the literature. In biomedical imaging application, breast cancer continues to be a public health problem. In this thesis we presented a computer aided diagnosis (CAD) system for mass detection and classification in digitized mammograms, which performs mass detection on regions of interest (ROI) followed by the benign-malignant classification on detected masses. Three layers ANN with seven features is proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist\u27s sensitivity 75%

USE OF IMAGE PROCESSING TECHNIQUES AND MACHINE LEARNING FOR BETTER UNDERSTANDING OF T GONDII BIOLOGY

Almost one in every three people worldwide is infected with Toxoplasma gondii (T. gondii). The biology and growth of the parasite’s bradyzoite form in host tissue cysts are not well understood. T. gondii’s metabolic state influences the morphology of its single mitochondrion, which can be visualized using fluorescence microscopy with specific dyes. Hence, fluorescence microscopy images of cysts purified from infected mouse brains carry biological information about bradyzoites, the poorly understood form of the parasite within them. With the help of fluorescence microscopy techniques, previous studies extracted images of the mitochondrion, nucleus, and the inner membrane complex (IMC) providing information on T. gondii’s cysts paving the way for image processing techniques and machine learning to analyze the bradyzoite form of the parasite. Previously, multivariate logistic regression (MLG) was used to classify shapes of mitochondrion. In the present study, in addition to the previously used MLG model, two other machine learning models, Support Vector Machine (SVM) and K Nearest Neighbors (KNN), were used to explore the possibility of better model selection for mitochondrial classification. A minimal model error was used to optimize the classification model performance. Error in any machine learning model is driven by bias, variance, and noise. Through trial and error, the optimal hyperparameters for each model were selected to minimize error. The dataset used consisted of 1940 labeled mitochondrial objects with 22 features, and consisted of five classes Blob, Tadpole, Donut, Arc, and Other. 50% of the dataset was used for training, and the other 50% was used for testing. The overall models’ accuracy of MLG, SVM, and KNN were 79.1%, 78.9%, and 80.3% respectively. Overall classification performance did not vary, but the F score for some classes like Tadpole and Donut showed improvement when using the two newer models. One of the 22 features used was an application of the Histogram of Oriented Gradients (HOG). The HOG feature was replaced with a novel feature that used linear regression of object boundary segments to extract the HOG for only the object’s boundary. The model that used Boundary HOG showed some improvement over the HOG feature. Finally, a new module including a graphical user interface was developed to process and extract shape and intensity information from TgIMC3 images which facilitate further investigations of the parasite biology