Classification of Medical Images Using Non-linear Distortion Models

In this paper, we address a problem of biomedical image classification that involves the automatic classification of x-ray images in 57 predefined classes with large intra-class variability. To achieve that goal, we apply and slightly adapt a recent generic method for image classification based on ensemble of decision trees and random subwindows [MGPW05]. We obtain classification results close to the state of the art on a publicly available database of 10000 x-ray images. We also provide some clues to interpret the classification of each image in terms of subwindow relevance.

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“…Misclassification error rate Method error rate 1-NN + IDM [KGN04] 12.6% 1-NN + CCF + IDM + Tamura 13.3% Discriminative patches [DKN05] 13 …”

Section: Protocol and Parametersmentioning

confidence: 99%

Biomedical Image Classification with Random Subwindows and Decision Trees

Marée

Geurts

Piater

et al. 2005

Computer Vision for Biomedical Image Applications

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“…The simplest visual image features are directly based on the pixel values of the image. Images are scaled to a common size and compared using Euclidean distance and image distortion model [13].Local features are extracted from small subimages from the original image. The global feature can be extracted to describe the whole image in an average fashion.…”

Section: Image Featuresmentioning

confidence: 99%

Content based Image Retrieval for Medical Images using Canny Edge Detection Algorithm

Ramamurthy¹,

Chandran²

2011

IJCA

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The rapid expansion of digital data content has led to the need for rich descriptions and efficient Retrieval Tool. To develop this, content based image Retrieval method has played an important role in the field of image retrieval. This paper aims to provide an efficient medical image data Retrieval from a huge content of medical database using one of the images content such as image shape, because, efficient content-based image Retrieval in the medical domain is still a challenging problem. The main objective of this paper is to provide an efficient tool which is used for efficient medical image retrieval from a huge content of medical image database and which is used for further medical diagnosis purposes.

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“…Since the scale and rotation variations in radiographs of the same category are small, the SIFT descriptor can not show its advantage of being scale and rotation invariant for describing radiographs. In previous works, local image patches have shown pleasant performance for medical image retrieval and classification [5][6] [7]. Therefore, we utilize local image patches as the local features in our experiments.…”

Section: Spatial Pyramid Matching For Medical Image Classificationmentioning

confidence: 99%

Medical Image Annotation and Retrieval Using Visual Features

Jing

Hiu

et al. 2007

Evaluation of Multilingual and Multi-Modal Information Retrieval

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In this article, we present the algorithms and results of our participation in the medical image annotation and retrieval tasks of ImageCLEFmed 2006. We exploit both global features and local features to describe medical images in the annotation task. We examine different kinds global features and extract the most descriptive ones, which effectively capture the intensity, texture and shape characters of the image content, to represent the radiographs. We also evaluate the descriptive power of local features, i.e. local image patches, for medical images. A newly developed spatial pyramid matching algorithm is applied to measure the similarity between images represented by sets of local features. Both descriptors use multi-class SVM to classify the images. The error rate is 17.6% for global description and 18.2% for the local one, which rank sixth and ninth respectively among all the submissions. For the medical image retrieval task, we only use visual features to describe the images. No textual information is considered. Different features are used to describe gray images and color images. Our submission achieves a mean average precision (MAP) of 0.0681, which ranks second in the 11 runs that also only use visual features.

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Classification of Medical Images Using Non-linear Distortion Models

Cited by 30 publications

References 6 publications

Biomedical Image Classification with Random Subwindows and Decision Trees

Biomedical Image Classification with Random Subwindows and Decision Trees

Content based Image Retrieval for Medical Images using Canny Edge Detection Algorithm

Medical Image Annotation and Retrieval Using Visual Features

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