Breast cancer diagnosis using an efficient CAD system based on multiple classifiers

Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100%, which was achieved with k-NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples

Radon Projections as Image Descriptors for Content-Based Retrieval of Medical Images

Clinical analysis and medical diagnosis of diverse diseases adopt medical imaging techniques to empower specialists to perform their tasks by visualizing internal body organs and tissues for classifying and treating diseases at an early stage. Content-Based Image Retrieval (CBIR) systems are a set of computer vision techniques to retrieve similar images from a large database based on proper image representations. Particularly in radiology and histopathology, CBIR is a promising approach to effectively screen, understand, and retrieve images with similar level of semantic descriptions from a database of previously diagnosed cases to provide physicians with reliable assistance for diagnosis, treatment planning and research. Over the past decade, the development of CBIR systems in medical imaging has expedited due to the increase in digitized modalities, an increase in computational efficiency (e.g., availability of GPUs), and progress in algorithm development in computer vision and artificial intelligence. Hence, medical specialists may use CBIR prototypes to query similar cases from a large image database based solely on the image content (and no text). Understanding the semantics of an image requires an expressive descriptor that has the ability to capture and to represent unique and invariant features of an image. Radon transform, one of the oldest techniques widely used in medical imaging, can capture the shape of organs in form of a one-dimensional histogram by projecting parallel rays through a two-dimensional object of concern at a specific angle. In this work, the Radon transform is re-designed to (i) extract features and (ii) generate a descriptor for content-based retrieval of medical images. Radon transform is applied to feed a deep neural network instead of raw images in order to improve the generalization of the network. Specifically, the framework is composed of providing Radon projections of an image to a deep autoencoder, from which the deepest layer is isolated and fed into a multi-layer perceptron for classification. This approach enables the network to (a) train much faster as the Radon projections are computationally inexpensive compared to raw input images, and (b) perform more accurately as Radon projections can make more pronounced and salient features to the network compared to raw images. This framework is validated on a publicly available radiography data set called "Image Retrieval in Medical Applications" (IRMA), consisting of 12,677 train and 1,733 test images, for which an classification accuracy of approximately 82% is achieved, outperforming all autoencoder strategies reported on the Image Retrieval in Medical Applications (IRMA) dataset. The classification accuracy is calculated by dividing the total IRMA error, a calculation outlined by the authors of the data set, with the total number of test images. Finally, a compact handcrafted image descriptor based on Radon transform was designed in this work that is called "Forming Local Intersections of Projections" (FLIP). The FLIP descriptor has been designed, through numerous experiments, for representing histopathology images. The FLIP descriptor is based on Radon transform wherein parallel projections are applied in a local 3x3 neighborhoods with 2 pixel overlap of gray-level images (staining of histopathology images is ignored). Using four equidistant projection directions in each window, the characteristics of the neighborhood is quantified by taking an element-wise minimum between each adjacent projection in each window. Thereafter, the FLIP histogram (descriptor) for each image is constructed. A multi-resolution FLIP (mFLIP) scheme is also proposed which is observed to outperform many state-of-the-art methods, among others deep features, when applied on the histopathology data set KIMIA Path24. Experiments show a total classification accuracy of approximately 72% using SVM classification, which surpasses the current benchmark of approximately 66% on the KIMIA Path24 data set

Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty

ADVANCED IMAGING ANALYSIS FOR PREDICTING TUMOR RESPONSE AND IMPROVING CONTOUR DELINEATION UNCERTAINTY By Rebecca Nichole Mahon, MS A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2018 Major Director: Dr. Elisabeth Weiss, Professor, Department of Radiation Oncology Radiomics, an advanced form of imaging analysis, is a growing field of interest in medicine. Radiomics seeks to extract quantitative information from images through use of computer vision techniques to assist in improving treatment. Early prediction of treatment response is one way of improving overall patient care. This work seeks to explore the feasibility of building predictive models from radiomic texture features extracted from magnetic resonance (MR) and computed tomography (CT) images of lung cancer patients. First, repeatable primary tumor texture features from each imaging modality were identified to ensure a sufficient number of repeatable features existed for model development. Then a workflow was developed to build models to predict overall survival and local control using single modality and multi-modality radiomics features. The workflow was also applied to normal tissue contours as a control study. Multiple significant models were identified for the single modality MR- and CT-based models, while the multi-modality models were promising indicating exploration with a larger cohort is warranted. Another way advances in imaging analysis can be leveraged is in improving accuracy of contours. Unfortunately, the tumor can be close in appearance to normal tissue on medical images creating high uncertainty in the tumor boundary. As the entire defined target is treated, providing physicians with additional information when delineating the target volume can improve the accuracy of the contour and potentially reduce the amount of normal tissue incorporated into the contour. Convolution neural networks were developed and trained to identify the tumor interface with normal tissue and for one network to identify the tumor location. A mock tool was presented using the output of the network to provide the physician with the uncertainty in prediction of the interface type and the probability of the contour delineation uncertainty exceeding 5mm for the top three predictions

Image texture analysis of transvaginal ultrasound in monitoring ovarian cancer

Ovarian cancer has the highest mortality rate of all gynaecologic cancers and is the fifth most common cancer in UK women. It has been dubbed “the silent killer” because of its non-specific symptoms. Amongst various imaging modalities, ultrasound is considered the main modality for ovarian cancer triage. Like other imaging modalities, the main issue is that the interpretation of the images is subjective and observer dependent. In order to overcome this problem, texture analysis was considered for this study. Advances in medical imaging, computer technology and image processing have collectively ramped up the interest of many researchers in texture analysis. While there have been a number of successful uses of texture analysis technique reported, to my knowledge, until recently it has yet to be applied to characterise an ovarian lesion from a B-mode image. The concept of applying texture analysis in the medical field would not replace the conventional method of interpreting images but is simply intended to aid clinicians in making their diagnoses. Five categories of textural features were considered in this study: grey-level co-occurrence matrix (GLCM), Run Length Matrix (RLM), gradient, auto-regressive (AR) and wavelet. Prior to the image classification, the robustness or how well a specific textural feature can tolerate variation arises from the image acquisition and texture extraction process was first evaluated. This includes random variation caused by the ultrasound system and the operator during image acquisition. Other factors include the influence of region of interest (ROI) size, ROI depth, scanner gain setting, and „calliper line‟. Evaluation of scanning reliability was carried out using a tissue-equivalent phantom as well as evaluations of a clinical environment. iii Additionally, the reliability of the ROI delineation procedure for clinical images was also evaluated. An image enhancement technique and semi-automatic segmentation tool were employed in order to improve the ROI delineation procedure. The results of the study indicated that two out of five textural features, GLCM and wavelet, were robust. Hence, these two features were then used for image classification purposes. To extract textural features from the clinical images, two ROI delineation approaches were introduced: (i) the textural features were extracted from the whole area of the tissue of interest, and (ii) the anechoic area within the normal and malignant tissues was excluded from features extraction. The results revealed that the second approach outperformed the first approach: there is a significant difference in the GLCM and wavelet features between the three groups: normal tissue, cysts, and malignant. Receiver operating characteristic (ROC) curve analysis was carried out to determine the discriminatory ability of textural features, which was found to be satisfactory. The principal conclusion was that GLCM and wavelet features can potentially be used as computer aided diagnosis (CAD) tools to help clinicians in the diagnosis of ovarian cancer

Developing novel quantitative imaging analysis schemes based machine learning for cancer research

The computer-aided detection (CAD) scheme is a developing technology in the medical imaging field, and it attracted extensive research interest in recent years. In this dissertation, I investigated the feasibility of developing several new novel CAD schemes for different cancer research purposes. First, I investigated the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to predict short-term breast cancer risk. For this study, an existing CAD scheme was applied “as is” to process each image. From CAD-generated results, some detection features were computed from each image. Two logistic regression models were then trained and tested using a leave-one-case-out cross-validation method to predict each testing case's likelihood of being positive in the next subsequent screening. This study demonstrated that CAD-generated false-positives contain valuable information to predict short-term breast cancer risk. Second, I identified and applied quantitative imaging features computed from ultrasound images of athymic nude mice to predict tumor response to treatment at an early stage. For this study, a CAD scheme was developed to perform tumor segmentation and image feature analysis. The study demonstrated the feasibility of extracting quantitative image features from the ultrasound images taken at an early treatment stage to predict tumor response to therapies. Last, I optimized a machine learning model for predicting peritoneal metastasis in gastric cancer. For this purpose, I have developed a CAD scheme to segment the tumor volume and extract quantitative image features automatically. Then, I reduced the dimensionality of features with a new method named random projection to optimize the model's performance. Finally, the gradient boosting machine model was applied along with a synthetic minority oversampling technique to predict peritoneal metastasis risk. Results suggested that the random projection method yielded promising results in improving the accuracy performance in peritoneal metastasis prediction. In summary, in my Ph.D. studies, I have investigated and tested several innovative approaches to develop different CAD schemes and identify quantitative imaging markers with high discriminatory power in various cancer research applications. Study results demonstrated the feasibility of applying CAD technology to several new application fields, which can help radiologists and gynecologists improve accuracy and consistency in disease diagnosis and prognosis assessment of using the medical image

Automated Resolution Selection for Image Segmentation

It is well known in image processing in general, and hence in image segmentation in particular, that computational cost increases rapidly with the number and dimensions of the images to be processed. Several fields, such as astronomy, remote sensing, and medical imaging, use very large images, which might also be 3D and/or captured at several frequency bands, all adding to the computational expense. Multiresolution analysis is one method of increasing the efficiency of the segmentation process. One multiresolution approach is the coarse-to-fine segmentation strategy, whereby the segmentation starts at a coarse resolution and is then fine-tuned during subsequent steps. Until now, the starting resolution for segmentation has been selected arbitrarily with no clear selection criteria. The research conducted for this thesis showed that starting from different resolutions for image segmentation results in different accuracies and speeds, even for images from the same dataset. An automated method for resolution selection for an input image would thus be beneficial. This thesis introduces a framework for the selection of the best resolution for image segmentation. First proposed is a measure for defining the best resolution based on user/system criteria, which offers a trade-off between accuracy and time. A learning approach is then described for the selection of the resolution, whereby extracted image features are mapped to the previously determined best resolution. In the learning process, class (i.e., resolution) distribution is imbalanced, making effective learning from the data difficult. A variant of AdaBoost, called RAMOBoost, is therefore used in this research for the learning-based selection of the best resolution for image segmentation. RAMOBoost is designed specifically for learning from imbalanced data. Two sets of features are used: Local Binary Patterns (LBP) and statistical features. Experiments conducted with four datasets using three different segmentation algorithms show that the resolutions selected through learning enable much faster segmentation than the original ones, while retaining at least the original accuracy. For three of the four datasets used, the segmentation results obtained with the proposed framework were significantly better than with the original resolution with respect to both accuracy and time