An Adaptive Algorithm to Identify Ambiguous Prostate Capsule Boundary Lines for Three-Dimensional Reconstruction and Quantitation

Currently there are few parameters that are used to compare the efficiency of different methods of cancerous prostate surgical removal. An accurate assessment of the percentage and depth of extra-capsular soft tissue removed with the prostate by the various surgical techniques can help surgeons determine the appropriateness of surgical approaches. Additionally, an objective assessment can allow a particular surgeon to compare individual performance against a standard. In order to facilitate 3D reconstruction and objective analysis and thus provide more accurate quantitation results when analyzing specimens, it is essential to automatically identify the capsule line that separates the prostate gland tissue from its extra-capsular tissue. However the prostate capsule is sometimes unrecognizable due to the naturally occurring intrusion of muscle and connective tissue into the prostate gland. At these regions where the capsule disappears, its contour can be arbitrarily reconstructed by drawing a continuing contour line based on the natural shape of the prostate gland. Presented here is a mathematical model that can be used in deciding the missing part of the capsule. This model approximates the missing parts of the capsule where it disappears to a standard shape by using a Generalized Hough Transform (GHT) approach to detect the prostate capsule. We also present an algorithm based on a least squares curve fitting technique that uses a prostate shape equation to merge previously detected capsule parts with the curve equation to produce an approximated curve that represents the prostate capsule. We have tested our algorithms using three shapes on 13 prostate slices that are cut at different locations from the apex and the results are promisin

Evaluation of Morphology Descriptors in CT images of the Aorta as Indicators of the Presence of Plaque

Thesis (M.A) -- Indiana University South Bend, 2009.This study compared the ability of six image descriptors, characterizing the morphology and elasticity of the descending aorta, to identify computed tomography (CT) images which contain visual indications of plaque. This thesis is based on the hypothesis that regions of plaque distort the normal lumen shape resulting in corresponding changes in the CT image. This, in turn, allows the inference of the presence of plaque by identifying deviations in the smoothness, symmetry, or circularity of the lumen border or by measurements that allow for an estimate of the elastic properties of the arterial wall. The project method included manually locating the descending aorta from a CT dataset, segmenting the lumen in each candidate slice, and computing descriptors from the resulting images. The descriptors computed are the lumen circularity, lumen centroid displacement, the area difference between the smallest enclosing circle and the lumen border, and the fractal dimension of the lumen border. In addition, the percentage expansion in lumen area and the dispersion of the lumen centroid were compared at the 0% and 40% gating in the R-R interval during the cardiac cycle. An assessment of the ability of each descriptor to identify the image slices containing potential plaque is included. The descriptors were measured against a reference set of images which were visually classified by domain experts. While each of the calculated descriptors was shown to have some merit, the circularity and the area difference between the smallest enclosing circle and the lumen border demonstrated the best individual performances in discriminating between the plaque and non-plaque images. The overall best predictive model was found by combining the strengths of the two descriptors

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Design, tuning and performance evaluation of an automated pulmonary nodule detection system

Radiologists miss about 25-30% of all pulmonary nodules smaller than 1.0 cm. in mass screenings. A system for the automated detection of the pulmonary nodule based on that of Hallard has been designed, tuned, and tested on a 43 chest radiographs [Ballard, 1973). The goal of this system is to aid the radiologist in locating a pulmonary nodule by indicating a few sites in the radiograph that are most likely to be nodules. Computer image analysis programs that respond to specific types of anatomic features have been devised and are incorporated in a pattern recognizer, which uses linear discriminant analysis to classify the candidate nodule sites. Candidate nodule sites that are not classified as nodules are eliminated from the list of sites that are presented to the radiologist for inspection. The pattern recognizer was trained with the features from 2750 candidate nodules, which came from 37 films and another pattern recognizer was trained with the features from 402 candidate nodules from 9 films. This research demonstrates that pattern recognition techniques and procedurally driven image experts are capable of reducing the number of candidate nodule sites that a radiologist must inspect from at most 12 to at most 4 if he is to be 99% confident of having inspected any nodule detected by the system which was trained with 37 films. The radiologist must be willing to accept a film true positive rate of 88% (as opposed to a film true positive rate of 92%) for the convenience of having fewer points to inspect. These film true positive rates are derived from 37 films which contain nodules that were evaluated by the system. The particular contributions of this work lies in the implementation and testing of a spline filter, a preprocessing step, which removes background variations in the radiograph so that nodules are more visible; the development of Vascularity and Rib Experts which recognize these classes of candidate nodules; and in die implementation of the particular features that are extracted from the candidate nodule and used by the pattern classifier

畳み込みニューラルネットワークと時空間サポートベクター回帰に基づくギターの運指の自動評価システム

早大学位記番号:新7965早稲田大

Sub-pixel Registration In Computational Imaging And Applications To Enhancement Of Maxillofacial Ct Data

In computational imaging, data acquired by sampling the same scene or object at different times or from different orientations result in images in different coordinate systems. Registration is a crucial step in order to be able to compare, integrate and fuse the data obtained from different measurements. Tomography is the method of imaging a single plane or slice of an object. A Computed Tomography (CT) scan, also known as a CAT scan (Computed Axial Tomography scan), is a Helical Tomography, which traditionally produces a 2D image of the structures in a thin section of the body. It uses X-ray, which is ionizing radiation. Although the actual dose is typically low, repeated scans should be limited. In dentistry, implant dentistry in specific, there is a need for 3D visualization of internal anatomy. The internal visualization is mainly based on CT scanning technologies. The most important technological advancement which dramatically enhanced the clinician\u27s ability to diagnose, treat, and plan dental implants has been the CT scan. Advanced 3D modeling and visualization techniques permit highly refined and accurate assessment of the CT scan data. However, in addition to imperfections of the instrument and the imaging process, it is not uncommon to encounter other unwanted artifacts in the form of bright regions, flares and erroneous pixels due to dental bridges, metal braces, etc. Currently, removing and cleaning up the data from acquisition backscattering imperfections and unwanted artifacts is performed manually, which is as good as the experience level of the technician. On the other hand the process is error prone, since the editing process needs to be performed image by image. We address some of these issues by proposing novel registration methods and using stonecast models of patient\u27s dental imprint as reference ground truth data. Stone-cast models were originally used by dentists to make complete or partial dentures. The CT scan of such stone-cast models can be used to automatically guide the cleaning of patients\u27 CT scans from defects or unwanted artifacts, and also as an automatic segmentation system for the outliers of the CT scan data without use of stone-cast models. Segmented data is subsequently used to clean the data from artifacts using a new proposed 3D inpainting approach