IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions

Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy

Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ROC curve for mini-MIAS are 91.39% and 0.964 respectively which give the confirmation of the effectiveness of the proposed algorithm (FCM-based SVM). This method increases the classification accuracy in the case of a malignant tumor. The simulation is based on R-software.This research was funded by the Spanish Government for its support through grant RTI2018-094336-B-100 (MCIU/AEI/FEDER, UE) and to the Basque Government for its support through grant IT1207-19

Ultrasound-guided Optical Techniques for Cancer Diagnosis: System and Algorithm Development

Worldwide, breast cancer is the most common cancer among women. In the United States alone, the American cancer society has estimated there will be 271,270 new breast cancer cases in 2019, and 42,260 lives will be lost to the disease. Ultrasound (US), mammography, and magnetic resonance imaging (MRI) are regularly used for breast cancer diagnosis and therapy monitoring. However, they sometimes fail to diagnose breast cancer effectively. These shortcomings have motivated researchers to explore new modalities. One of these modalities, diffuse optical tomography (DOT), utilizes near-infrared (NIR) light to reveal the optical properties of tissue. NIR-based DOT images the contrast between a suspected lesion’s location and the background tissue, caused by the higher NIR absorption of the hemoglobin which characterizes tumors. The limitation of high light scattering inside tissue is minimized by using ultrasound image to find the tumor location. This thesis focuses on developing a compact, low-cost ultrasound guided diffuse optical tomography imaging system and on improving optical image reconstruction by extracting the tumor’s location and size from co-registered ultrasound images. Several electronic components have been redesigned and optimized to save space and cost and to improve the user experience. In terms of software and algorithm development, manual extraction of tumor information from ultrasound images has been replaced by using a semi-automated ultrasound image segmentation algorithm that reduces the optical image reconstruction time and operator dependency. This system and algorithm have been validated with phantom and clinical data and have demonstrated their efficacy. An ongoing clinical trial will continue to gather more patient data to improve the robustness of the imaging algorithm. Another part of this research focuses on ovarian cancer diagnosis. Ovarian cancer is the most deadly of all gynecological cancers, with a less than 50% five-year survival rate. This cancer can evolve without any noticeable symptom, which makes it difficult to diagnose in an early stage. Although ultrasound-guided photoacoustic tomography (PAT) has demonstrated potential for early detection of ovarian cancer, clinical studies have been very limited due to the lack of robust PAT systems. In this research, we have customized a commercial ultrasound system to obtain real-time co-registered PAT and US images. This system was validated with several phantom studies before use in a clinical trial. PAT and US raw data from 30 ovarian cancer patients was used to extract spectral and statistical features for training and testing classifiers for automatic diagnosis. For some challenging cases, the region of interest selection was improved by reconstructing co-registered Doppler images. This study will be continued in order to obtain quantitative tissue properties using US-guided PAT

A similarity study of contentâ based image retrieval system for breast cancer using decision tree

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134877/1/mp0277.pd

Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging