Model attraction in medical image object recognition

Guido Tascini; Primo Zingaretti

doi:10.1117/12.206849

17 April 1995 Model attraction in medical image object recognition

Guido Tascini, Primo Zingaretti

Proceedings Volume 2436, Medical Imaging 1995: Image Perception; (1995) https://doi.org/10.1117/12.206849
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

Abstract

This paper presents as new approach to image recognition based on a general attraction principle. A cognitive recognition is governed by a 'focus on attention' process that concentrates on the visual data subset of task- relevant type only. Our model-based approach combines it with another process, focus on attraction, which concentrates on the transformations of visual data having relevance for the matching. The recognition process is characterized by an intentional evolution of the visual data. This chain of image transformations is viewed as driven by an attraction field that attempts to reduce the distance between the image-point and the model-point in the feature space. The field sources are determined during a learning phase, by supplying the system with a training set. The paper describes a medical interpretation case in the feature space, concerning human skin lesions. The samples of the training set, supplied by the dermatologists, allow the system to learn models of lesions in terms of features such as hue factor, asymmetry factor, and asperity factor. The comparison of the visual data with the model derives the trend of image transformations, allowing a better definition of the given image and its classification. The algorithms are implemented in C language on a PC equipped with Matrox Image Series IM-1280 acquisition and processing boards. The work is now in progress.

Citation Download Citation

Guido Tascini and Primo Zingaretti "Model attraction in medical image object recognition", Proc. SPIE 2436, Medical Imaging 1995: Image Perception, (17 April 1995); https://doi.org/10.1117/12.206849

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KEYWORDS

Visualization

Visual process modeling

Data modeling

Image processing

Image segmentation

Skin

Medical imaging

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