Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Preprocessing of microcirculatory images.

This thesis project consists of the development of a computerized image processing system to preprocess microvascular images of the mouse Latisimus Dorsi Muscle (LDM). This research has been conducted in association with the Division of Plastic and Reconstructive Surgery at the University of Louisville. The input to the system is a set of 35 or more overlapping microscopic fields or sub_images containing segments of the LDM microcirculation, the output is an adjacent single montage encompassing the entire LDM microvasculature. The developed system presents practical solutions to the problems of image registration, overlap resolution, and image segmentation, in addition, the results of this project include simple geometrical measurements of the microvascular system such as length, tortuosity, and directionality. In future projects, the measurements should be further analyzed to study the LDM microcirculation. The entire system was written in C/C++ languages, and the Bluestone\u27s UIM/X GUI builder. The system was implemented on the Speed School\u27s HP-9000 Unix system. The system also takes advantage of the image processing libraries (ImageMagick) available on the system

Text Extraction in Video

The detection and extraction of scene and caption text from unconstrained, general purpose video is an important research problem in the context of content-based retrieval and summarization of visual information. The current state of the art for extracting text from video either makes simplistic assumptions as to the nature of the text to be found, or restricts itself to a subclass of the wide variety of text that can occur in broadcast video. Most published methods only work on artificial text (captions) that is composited on the video frame. Also, these methods have been developed for extracting text from images that have been applied to video frames. They do not use the additional temporal information in video to good effect.This thesis presents a reliable system for detecting, localizing, extracting, tracking and binarizing text from unconstrained, general-purpose video. In developing methods for extraction of text from video it was observed that no single algorithm could detect all forms of text. The strategy is to have a multi-pronged approach to the problem, one that involves multiple methods, and algorithms operating in functional parallelism. The system utilizes the temporal information available in video. The system can operate on JPEG images, MPEG-1 bit streams, as well as live video feeds. It is also possible to operate the methods individually and independently

Human treelike tubular structure segmentation: A comprehensive review and future perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed

Ameliorating the Saliency of Objects in digital Images

Visual saliency is the capability of a revelation system of human or machine to choose a sub part of information for further processing. The mechanism describes here serves as a filter to pick only the interesting information related to given errands and the tasks while ignoring inappropriate information. This work uses Random Forest to know the similarity between the image patches, apply active contour model to get the approximate contour and do dynamic thresholding segmentation .The results we get consists of many small artifacts, so to remove the low level details and to obtain the more smoothness we apply gradient minimization technique

Text Detection in Natural Scenes and Technical Diagrams with Convolutional Feature Learning and Cascaded Classification

An enormous amount of digital images are being generated and stored every day. Understanding text in these images is an important challenge with large impacts for academic, industrial and domestic applications. Recent studies address the difficulty of separating text targets from noise and background, all of which vary greatly in natural scenes. To tackle this problem, we develop a text detection system to analyze and utilize visual information in a data driven, automatic and intelligent way. The proposed method incorporates features learned from data, including patch-based coarse-to-fine detection (Text-Conv), connected component extraction using region growing, and graph-based word segmentation (Word-Graph). Text-Conv is a sliding window-based detector, with convolution masks learned using the Convolutional k-means algorithm (Coates et. al, 2011). Unlike convolutional neural networks (CNNs), a single vector/layer of convolution mask responses are used to classify patches. An initial coarse detection considers both local and neighboring patch responses, followed by refinement using varying aspect ratios and rotations for a smaller local detection window. Different levels of visual detail from ground truth are utilized in each step, first using constraints on bounding box intersections, and then a combination of bounding box and pixel intersections. Combining masks from different Convolutional k-means initializations, e.g., seeded using random vectors and then support vectors improves performance. The Word-Graph algorithm uses contextual information to improve word segmentation and prune false character detections based on visual features and spatial context. Our system obtains pixel, character, and word detection f-measures of 93.14%, 90.26%, and 86.77% respectively for the ICDAR 2015 Robust Reading Focused Scene Text dataset, out-performing state-of-the-art systems, and producing highly accurate text detection masks at the pixel level. To investigate the utility of our feature learning approach for other image types, we perform tests on 8- bit greyscale USPTO patent drawing diagram images. An ensemble of Ada-Boost classifiers with different convolutional features (MetaBoost) is used to classify patches as text or background. The Tesseract OCR system is used to recognize characters in detected labels and enhance performance. With appropriate pre-processing and post-processing, f-measures of 82% for part label location, and 73% for valid part label locations and strings are obtained, which are the best obtained to-date for the USPTO patent diagram data set used in our experiments. To sum up, an intelligent refinement of convolutional k-means-based feature learning and novel automatic classification methods are proposed for text detection, which obtain state-of-the-art results without the need for strong prior knowledge. Different ground truth representations along with features including edges, color, shape and spatial relationships are used coherently to improve accuracy. Different variations of feature learning are explored, e.g. support vector-seeded clustering and MetaBoost, with results suggesting that increased diversity in learned features benefit convolution-based text detectors