Mammographic image classification using histogram intersection

Cheng, Erkang; Xie, Nianhua; Ling, Haibin; Bakić, Predrag R.; Maidment, Andrew D. A.; Megalooikonomou, Vasileios

doi:10.1109/isbi.2010.5490381

Cited by 19 publications

(10 citation statements)

References 16 publications

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“…Specifically, our experiments involve four different similarity measures and three classifiers, resulting in ten different approachess: (1) l1+KNN, (2) Normalized l1+KNN [1], (3) HI+KNN, (4) Normalized HI+KNN, (5) HI+SVM, (6) Normalized HI+SVM, (7) l1 + NN, (8) Normalized l1 + NN, (9) HI+NN, and (10) Normalized HI+NN. Each method was applied on datasets R1, L1 and R1+L1 described in Section 3.1.…”

Section: Methodsmentioning

confidence: 99%

“…This histogram similarity measure is denoted as s NL1 because it is closely related to the "Normalized" l 1 distance. In this study, we also compare previously proposed [1] measure of similarity using histogram intersection (HI), which is defined as…”

Section: Histogram Similaritymentioning

confidence: 99%

“…Although HI can be used directly as a similarity measure, it suffers from the problem where histograms are very noisy. Therefore, we use a Normalized HI (NHI) [1] defined as…”

Section: Histogram Similaritymentioning

confidence: 99%

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Classification of texture patterns in CT lung imaging

et al. 2011

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Since several lung diseases can be potentially diagnosed based on the patterns of lung tissue observed in medical images, automated texture classification can be useful in assisting the diagnosis. In this paper, we propose a methodology for discriminating between various types of normal and diseased lung tissue in computed tomography (CT) images that utilizes Vector Quantization (VQ), an image compression technique, to extract discriminative texture features. Rather than focusing on images of the entire lung, we direct our attention to the extraction of local descriptors from individual regions of interest (ROIs) as determined by domain experts. After determining the ROIs, we generate "locally optimal" codebooks representing texture features of each region using the Generalized Lloyd Algorithm. We then utilize the codeword usage frequency of each codebook as a discriminative feature vector for the region it represents. We compare k-nearest neighbor, support vector machine and neural network classification approaches using the normalized histogram intersection as a similarity measure. The classification accuracy reached up to 98% for certain experimental settings, indicating that our approach may potentially assist clinicians in the interpretation of lung images and facilitate the investigation of relationships among structure, texture and function or pathology related to several lung diseases.

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Section: Methodsmentioning

confidence: 99%

Section: Histogram Similaritymentioning

confidence: 99%

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Classification of texture patterns in CT lung imaging

et al. 2011

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“…Biomedical image classication algorithms often use more advanced classiers such as K-nearest neighbors and support vector machines [3], that could also provide better results. The primary aim of this book chapter was to introduce the framework for local Tchebichef moments, and therefore detailed optimization aspects have been left out.…”

Section: Texture Classicationmentioning

confidence: 99%

Local Tchebichef Moments for Texture Analysis

Mukundan

2014

Moments and Moment Invariants - Theory and Applications

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Orthogonal moment functions based on Tchebichef polynomials have found several applications in the eld of image analysis because of their superior feature representation capabilities. Local features represented by such moments could also be used in the design of ecient texture descriptors. This chapter introduces a novel method of constructing feature vectors from orthonormal Tchebichef moments evaluated on 5 × 5 neighborhoods of pixels, and encoding the texture information as a Lehmer code that represents the relative strengths of the evaluated moments. The features will be referred to as Local Tchebichef Moments (LTMs). The encoding scheme provides a byte value for each pixel, and generates a gray-level LTM-image of the input image. The histogram of the LTM-image is then used as the texture descriptor for classication.The theoretical framework as well as the implementation aspects of the descriptor are discussed in detail.

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“…In this method using histogram intersection and SVM a classification accuracy of 73.3% was achieved. Wang reports that this bag-of-visual-words approach is successfully applied for classifying the breast images into BI_RADS categories [41]. This approach has also shown to be useful in lung image classification [42].…”

Section: Literature Reviewmentioning

confidence: 99%