Computer-Aided Diagnosis in Neuroimaging

This chapter is intended to provide an overview to the most used methods for computer-aided diagnosis in neuroimaging and its application to neurodegenerative diseases. The fundamental preprocessing steps, and how they are applied to different image modalities, will be thoroughly presented. We introduce a number of widely used neuroimaging analysis algorithms, together with a wide overview on the recent advances in brain imaging processing. Finally, we provide a general conclusion on the state of the art in brain imaging processing and possible future developments

Detection of prostate cancer using multi-parametric magnetic resonance

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (leaves 26-28).A multi-channel statistical classifier to detect prostate cancer was developed by combining information from 3 different MR methodologies: T2-weighted, T2-mapping, and Line Scan Diffusion lmaging(LSDI). From these MR sequences, 4 sets of image intensities were obtained: T2-weighted(T2W) from T2-weighted imaging, Apparent Diffusion Coefficient(ADC) from LSDI, and Proton Density (PD) and T2 (T2Map) from T2-mapping imaging. Manually- segmented tumor labels from a radiologist were validated by biopsy results to serve as tumor "ground truth." Textural features were derived from the images using co-occurrence matrix and discrete cosine transform. Anatomical location of voxels was described by a cylindrical coordinate system. Statistical jack-knife approach was used to evaluate our classifiers. Single-channel maximum likelihood(ML) classifiers were based on 1 of the 4 basic image intensities. Our multi-channel classifiers: support vector machine (SVM) and fisher linear discriminant(FLD), utilized 5 different sets of derived features. Each classifer generated a summary statistical map that indicated tumor likelihood in the peripheral zone(PZ) of the gland. To assess classifier accuracy, the average areas under the receiver operator characteristic (ROC) curves were compared. Our best FLD classifier achieved an average ROC area of 0.839 (±0.064) and our best SVM classifier achieved an average ROC area of 0.761 (±0.043). The T2W intensity maximum likelihood classifier, our best single-channel classifier, only achieved an average ROC area of 0.599 (± 0.146). Compared to the best single-channel ML classifier, our best multi-channel FLD and SVM classifiers have statistically superior ROC performance with P-values of 0.0003 and 0.0017 respectively from pairwise 2-sided t-test. By integrating information from the multiple images and capturing the textural and anatomical features in tumor areas, the statistical summary maps can potentially improve the accuracy of image-guided prostate biopsy and enable the delivery of localized therapy under image guidance.by Ian Chan.M.Eng

Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics

Current research in the life sciences involves the analysis of such a huge amount of image data that automatization is required. This thesis presents several ways how pattern recognition techniques may contribute to improved tumor diagnostics and to the elucidation of vertebrate embryonic development. Chapter 1 studies an approach for exploiting spatial context for the improved estimation of metabolite concentrations from magnetic resonance spectroscopy imaging (MRSI) data with the aim of more robust tumor detection, and compares against a novel alternative. Chapter 2 describes a software library for training, testing and validating classification algorithms that estimate tumor probability based on MRSI. It allows flexible adaptation towards changed experimental conditions, classifier comparison and quality control without need for expertise in pattern recognition. Chapter 3 studies several models for learning tumor classifiers that allow for the common unreliability of human segmentations. For the first time, models are used for this task that additionally employ the objective image information. Chapter 4 encompasses two contributions to an image analysis pipeline for automatically reconstructing zebrafish embryonic development based on time-resolved microscopy: Two approaches for nucleus segmentation are experimentally compared, and a procedure for tracking nuclei over time is presented and evaluated