CLASSIFICATION OF TAGGED MATERIAL IN A SET OF TOMOGRAPHIC IMAGES OF COLORECTAL REGION

method of classification of image portions corresponding to faecal residues from a tomographic image of a colorectal region, which comprises a plurality of voxels (2) each having a predetermined intensity value and which shows at least one portion of colon (6a, 6b, 6c, 6d) comprising at least one area of tagged material (10). The area of tagged material (10) comprises at least one area of faecal residue (10a) and at least one area of tissue affected by tagging (10b). The image further comprises at least one area of air (8) which comprises an area of pure air (8a) not influenced by the faecal residues. The method comprises the operations of identifying (100), on the basis of a predetermined identification criterion based on the intensity values, above-threshold connected regions comprising connected voxels (2) and identifying, within the above-threshold connected regions, a plurality of connected regions of tagged material comprising voxels (2) representing the area of tagged material (10). The method further comprises the operation of classifying (104) each plurality of connected regions of tagged material on the basis of specific classification comparison criteria for each connected region, in such a way as to identify voxels (20) corresponding to the area of faecal residue (10a) and voxels (2) corresponding to the area of tissue affected by tagging (10b)

Method of classification of tagged material in a set of tomographic images of colorectal region

A method of classification of image portions corresponding to fecal residues from a tomographic image of a colorectal region, which comprises a plurality of voxels (2) each having a predetermined intensity value and which shows at least one portion of colon (6 a, 6 b, 6 c, 6 d) comprising at least one area of tagged material (10). The area of tagged material (10) comprises at least one area of fecal residue (10 a) and at least one area of tissue affected by tagging (10 b). The image further comprises at least one area of air (8) which comprises an area of pure air (8 a) not influenced by the fecal residues. The method comprises the operations of identifying (100), on the basis of a predetermined identification criterion based on the intensity values, above-threshold connected regions comprising connected voxels (2) and identifying, within the above-threshold connected regions, a plurality of connected regions of tagged material comprising voxels (2) representing the area of tagged material (10). The method further comprises the operation of classifying (104) each plurality of connected regions of tagged material on the basis of specific classification comparison criteria for each connected region, in such a way as to identify voxels (20) corresponding to the area of fecal residue (10 a) and voxels (2) corresponding to the area of tissue affected by tagging (10 b)

Minnesota Tree Line: How to Buy a Tree, no.1, 1977

This archival publication may not reflect current scientific knowledge or recommendations. Current information available from the University of Minnesota Extension: https://www.extension.umn.edu

Computer-Aided Diagnosis for X-ray Imaging

This chapter provides an overview of the operating principles of computer-aided detection (CAD) systems, with specific focus on breast and colon cancer diagnosis. It shows how advances in X-ray imaging modalities reflect on CAD technology, as for instance with the shift from two-dimensional mammography to three-dimensional tomosynthesis. The chapter discusses how CAD technology can be integrated with the radiologist's workflow according to different paradigms, and its impact on accuracy and efficacy. It describes principles of clinical validation and practical implementation of CAD. CAD applications are being developed for a multitude of imaging technologies and clinical applications. Most CAD applications are implemented through a pipeline of image processing and pattern recognition modules. A typical computed tomography (CT) Colonography CAD systems comprises a first phase, in which digital cleansing and colon surface extraction is performed, and a second phase, in which CAD candidates are segmented and filtered

Inverse problems and genetic algorithms

Inverse problems are omnipresent in natural and engineering sciences, for example, in material characterization. Impressive results have been obtained by applying analytical–numerical techniques to their solution; however, in many practical cases these methods present drawbacks, which impede their application. In this scenario, Genetic Algorithms (GAs) arise as interesting alternatives, especially for the solution of complicated inverse problems, such as those resulting from the modeling and characterization of complex nonlinear systems, such as in particular materials with nonlinear elastic behavior. In this chapter, we present a brief introduction to inverse problem solution, highlighting the difficulties inherent in the application of traditional analytical–numerical techniques, and illustrating how genetic algorithms may in part obviate these problem