Vertebra Shape Classification using MLP for Content-Based Image Retrieval

A desirable content-based image retrieval (CBIR) system would classify extracted image features to support some form of semantic retrieval. The Lister Hill National Center for Biomedical Communications, an intramural R&D division of the National Library for Medicine (NLM), maintains an archive of digitized X-rays of the cervical and lumbar spine taken as part of the second national health and nutrition examination survey (NHANES II). It is our goal to provide shape-based access to digitized X-rays including retrieval on automatically detected and classified pathology, e.g., anterior osteophytes. This is done using radius of curvature analysis along the anterior portion, and morphological analysis for quantifying protrusion regions along the vertebra boundary. Experimental results are presented for the classification of 704 cervical spine vertebrae by evaluating the features using a multi-layer perceptron (MLP) based approach. In this paper, we describe the design and current status of the content-based image retrieval (CBIR) system and the role of neural networks in the design of an effective multimedia information retrieval system

Applying Vertebral Boundary Semantics to CBIR of Digitized Spine X-ray Images

There is a growing research interest in reliable content-based image retrieval (CBIR) techniques specialized for biomedical image retrieval. Applicable feature representation and similarity algorithms have to balance conflicting goals of efficient and effective retrieval while allowing queries on important and often subtle biomedical features. In a collection of digitized X-rays of the spine, such as that from the second National Health and Nutrition Examination Survey (NHANES II) maintained by the National Library of Medicine, a typical user may be interested in only a small region of the vertebral boundary pertinent to the pathology: for this experiment, the Anterior Osteophyte (AO). A previous experiment in pathology-based retrieval using partial shape matching (PSM) on a subset from the above collection; about 89 % normal vertebrae were correctly retrieved. In contrast only 45 % of moderate and severe cases were correctly retrieved, and on the average only 46 % of the pathology classes were correctly determined. Further analysis revealed that mere shape matching is insufficient for semantically correct retrieval of pathological cases. This paper describes an automatic 9 point localization algorithm that incorporates reasoning about boundary semantics equivalent to that applied by the content-expert as a step in our enhancements to PSM, and results from initial experiments

WICC 2016 : XVIII Workshop de Investigadores en Ciencias de la Computación

Actas del XVIII Workshop de Investigadores en Ciencias de la Computación (WICC 2016), realizado en la Universidad Nacional de Entre Ríos, el 14 y 15 de abril de 2016.Red de Universidades con Carreras en Informática (RedUNCI