Developing the Quantitative Histopathology Image Ontology : A case study using the hot spot detection problem

Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology – QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Guest Editorial to the Special Letters Issue on Emerging Technologies in Multiparameter Biomedical Optical Imaging and Image Analysis

The past two decades have witnessed revolutionary advances in biomedical imaging modalities capable of providing biological and physiological information from the cellular scale to the organ level. Recent advances have also been focused on cost-effective, noninvasive, portable, and molecularimaging technologies for imaging at microscopic, mesoscopic, and macroscopic levels. These technologies have significant potential to advance biomedical research and clinical practice. They can also provide a better understanding and monitoring of physiological and functional disorders, which could lead to mainstream diagnostic technologies of the future

Image Analysis for Cystic Fibrosis: Computer-Assisted Airway Wall and Vessel Measurements from Low-Dose, Limited Scan Lung CT Images

Cystic fibrosis (CF) is a life-limiting genetic disease that affects approximately 30,000 Americans. When compared to those of normal children, airways of infants and young children with CF have thicker walls and are more dilated in high-resolution computed tomographic (CT) imaging. In this study, we develop computer-assisted methods for assessment of airway and vessel dimensions from axial, limited scan CT lung images acquired at low pediatric radiation doses. Two methods (threshold- and model-based) were developed to automatically measure airway and vessel sizes for pairs identified by a user. These methods were evaluated on chest CT images from 16 pediatric patients (eight infants and eight children) with different stages of mild CF related lung disease. Results of threshold-based, corrected with regression analysis, and model-based approaches correlated well with both electronic caliper measurements made by experienced observers and spirometric measurements of lung function. While the model-based approach results correlated slightly better with the human measurements than those of the threshold method, a hybrid method, combining these two methods, resulted in the best results

Morphological subband decomposition structure using GF(N) arithmetic

Linear filter banks with critical subsampling and perfect reconstruction (PR) property have received much interest and found numerous applications in signal and image processing. Recently, nonlinear filter bank structures with PR and critical subsampling have been proposed and used in image coding. In this paper, it is shown that PR nonlinear subband decomposition can be performed using the Gallois Field (GF) arithmetic. The result of the decomposition of an n-ary (e.g. 256-ary) input signal is still n-ary at different resolutions. This decomposition structure can be utilized for binary and 2k (k is an integer) level signal decompositions. Simulation studies are presented

Guest Editorial to the Special Letters Issue on Emerging Technologies in Multiparameter Biomedical Optical Imaging and Image Analysis

The past two decades have witnessed revolutionary advances in biomedical imaging modalities capable of providing biological and physiological information from the cellular scale to the organ level. Recent advances have also been focused on cost-effective, noninvasive, portable, and molecularimaging technologies for imaging at microscopic, mesoscopic, and macroscopic levels. These technologies have significant potential to advance biomedical research and clinical practice. They can also provide a better understanding and monitoring of physiological and functional disorders, which could lead to mainstream diagnostic technologies of the future

Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computerâ aided diagnosis system

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135039/1/mp5762.pd

GridIMAGE: A Novel Use of Grid Computing to Support Interactive Human and Computer-Assisted Detection Decision Support

This paper describes a Grid-aware image reviewing system (GridIMAGE) that allows practitioners to (a) select images from multiple geographically distributed digital imaging and communication in medicine (DICOM) servers, (b) send those images to a specified group of human readers and computer-assisted detection (CAD) algorithms, and (c) obtain and compare interpretations from human readers and CAD algorithms. The currently implemented system was developed using the National Cancer Institute caGrid infrastructure and is designed to support the identification of lung nodules on thoracic computed tomography. However, the infrastructure is general and can support any type of distributed review. caGrid data and analytical services are used to link DICOM image databases and CAD systems and to interact with human readers. Moreover, the service-oriented and distributed structure of the GridIMAGE framework enables a flexible system, which can be deployed in an institution (linking multiple DICOM servers and CAD algorithms) and in a Grid environment (linking the resources of collaborating research groups). GridIMAGE provides a framework that allows practitioners to obtain interpretations from one or more human readers or CAD algorithms. It also provides a mechanism to allow cooperative imaging groups to systematically perform image interpretation tasks associated with research protocols

Selection of an optimal neural network architecture for computerâ aided detection of microcalcificationsâ Comparison of automated optimization techniques

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134870/1/mp5036.pd