Computer-Aided Cancer Diagnosis and Grading via Sparse Directional Image Representations

Prostate cancer and breast cancer are the second cause of death among cancers in males and females, respectively. If not diagnosed, prostate and breast cancers can spread and metastasize to other organs and bones and make it impossible for treatment. Hence, early diagnosis of cancer is vital for patient survival. Histopathological evaluation of the tissue is used for cancer diagnosis. The tissue is taken during biopsies and stained using hematoxylin and eosin (H&E) stain. Then a pathologist looks for abnormal changes in the tissue to diagnose and grade the cancer. This process can be time-consuming and subjective. A reliable and repetitive automatic cancer diagnosis method can greatly reduce the time while producing more reliable results. The scope of this dissertation is developing computer vision and machine learning algorithms for automatic cancer diagnosis and grading methods with accuracy acceptable by the expert pathologists. Automatic image classification relies on feature representation methods. In this dissertation we developed methods utilizing sparse directional multiscale transforms - specifically shearlet transform - for medical image analysis. We particularly designed theses computer visions-based algorithms and methods to work with H&E images and MRI images. Traditional signal processing methods (e.g. Fourier transform, wavelet transform, etc.) are not suitable for detecting carcinoma cells due to their lack of directional sensitivity. However, shearlet transform has inherent directional sensitivity and multiscale framework that enables it to detect different edges in the tissue images. We developed techniques for extracting holistic and local texture features from the histological and MRI images using histogram and co-occurrence of shearlet coefficients, respectively. Then we combined these features with the color and morphological features using multiple kernel learning (MKL) algorithm and employed support vector machines (SVM) with MKL to classify the medical images. We further investigated the impact of deep neural networks in representing the medical images for cancer detection. The aforementioned engineered features have a few limitations. They lack generalizability due to being tailored to the specific texture and structure of the tissues. They are time-consuming and expensive and need prepossessing and sometimes it is difficult to extract discriminative features from the images. On the other hand, feature learning techniques use multiple processing layers and learn feature representations directly from the data. To address these issues, we have developed a deep neural network containing multiple layers of convolution, max-pooling, and fully connected layers, trained on the Red, Green, and Blue (RGB) images along with the magnitude and phase of shearlet coefficients. Then we developed a weighted decision fusion deep neural network that assigns weights on the output probabilities and update those weights via backpropagation. The final decision was a weighted sum of the decisions from the RGB, and the magnitude and the phase of shearlet networks. We used the trained networks for classification of benign and malignant H&E images and Gleason grading. Our experimental results show that our proposed methods based on feature engineering and feature learning outperform the state-of-the-art and are even near perfect (100%) for some databases in terms of classification accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC) and hence are promising computer-based methods for cancer diagnosis and grading using images

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Meningioma classification using an adaptive discriminant wavelet packet transform

Meningioma subtypes classification is a real world problem from the domain of histological image analysis that requires new methods for its resolution. Computerised histopathology presents a whole new set of problems and introduces new challenges in image classification. High intra-class variation and low inter-class differences in textures is often an issue in histological image analysis problems such as Meningioma subtypes classification. In this thesis, we present an adaptive wavelets based technique that adapts to the variation in the texture of meningioma samples and provides high classification accuracy results. The technique provides a mechanism for attaining an image representation consisting of various spatial frequency resolutions that represent the image and are referred to as subbands. Each subband provides different information pertaining to the texture in the image sample. Our novel method, the Adaptive Discriminant Wavelet Packet Transform (ADWPT), provides a means for selecting the most useful subbands and hence, achieves feature selection. It also provides a mechanism for ranking features based upon the discrimination power of a subband. The more discriminant a subband, the better it is for classification. The results show that high classification accuracies are obtained by selecting subbands with high discrimination power. Moreover, subbands that are more stable i.e. have a higher probability of being selected provide better classification accuracies. Stability and discrimination power have been shown to have a direct relationship with classification accuracy. Hence, ADWPT acquires a subset of subbands that provide a highly discriminant and robust set of features for Meningioma subtype classification. Classification accuracies obtained are greater than 90% for most Meningioma subtypes. Consequently, ADWPT is a robust and adaptive technique which enables it to overcome the issue of high intra-class variation by statistically selecting the most useful subbands for meningioma subtype classification. It overcomes the issue of low inter-class variation by adapting to texture samples and extracting the subbands that are best for differentiating between the various meningioma subtype textures

Cascade of classifier ensembles for reliable medical image classification

Medical image analysis and recognition is one of the most important tools in modern medicine. Different types of imaging technologies such as X-ray, ultrasonography, biopsy, computed tomography and optical coherence tomography have been widely used in clinical diagnosis for various kinds of diseases. However, in clinical applications, it is usually time consuming to examine an image manually. Moreover, there is always a subjective element related to the pathological examination of an image. This produces the potential risk of a doctor to make a wrong decision. Therefore, an automated technique will provide valuable assistance for physicians. By utilizing techniques from machine learning and image analysis, this thesis aims to construct reliable diagnostic models for medical image data so as to reduce the problems faced by medical experts in image examination. Through supervised learning of the image data, the diagnostic model can be constructed automatically. The process of image examination by human experts is very difficult to simulate, as the knowledge of medical experts is often fuzzy and not easy to be quantified. Therefore, the problem of automatic diagnosis based on images is usually converted to the problem of image classification. For the image classification tasks, using a single classifier is often hard to capture all aspects of image data distributions. Therefore, in this thesis, a classifier ensemble based on random subspace method is proposed to classify microscopic images. The multi-layer perceptrons are used as the base classifiers in the ensemble. Three types of feature extraction methods are selected for microscopic image description. The proposed method was evaluated on two microscopic image sets and showed promising results compared with the state-of-art results. In order to address the classification reliability in biomedical image classification problems, a novel cascade classification system is designed. Two random subspace based classifier ensembles are serially connected in the proposed system. In the first stage of the cascade system, an ensemble of support vector machines are used as the base classifiers. The second stage consists of a neural network classifier ensemble. Using the reject option, the images whose classification results cannot achieve the predefined rejection threshold at the current stage will be passed to the next stage for further consideration. The proposed cascade system was evaluated on a breast cancer biopsy image set and two UCI machine learning datasets, the experimental results showed that the proposed method can achieve high classification reliability and accuracy with small rejection rate. Many computer aided diagnosis systems face the problem of imbalance data. The datasets used for diagnosis are often imbalanced as the number of normal cases is usually larger than the number of the disease cases. Classifiers that generalize over the data are not the most appropriate choice in such an imbalanced situation. To tackle this problem, a novel one-class classifier ensemble is proposed. The Kernel Principle Components are selected as the base classifiers in the ensemble; the base classifiers are trained by different types of image features respectively and then combined using a product combining rule. The proposed one-class classifier ensemble is also embedded into the cascade scheme to improve classification reliability and accuracy. The proposed method was evaluated on two medical image sets. Favorable results were obtained comparing with the state-of-art results

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Intelligent System for Breast Cancer Prognosis using Multiwavelet Packets and Neural Network

Abstract—This paper presents an approach for early breast cancer diagnostic by employing combination of artificial neural networks (ANN) and multiwaveletpacket based subband image decomposition. The microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands,, reconstructing the mammograms from the subbands containing only high frequencies. For this approach we employed different types of multiwaveletpacket. We used the result as an input of neural network for classification. The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases and images collected from local hospitals. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve. Keywords—Breast cancer, neural networks, diagnosis, multiwavelet packet, microcalcification. I