Histopathology Image Classification Using Bag of Features and Kernel Functions

Caicedo, Juan C.; Cruz, Angel; González, Fabio A.

doi:10.1007/978-3-642-02976-9_17

Cited by 144 publications

(93 citation statements)

References 13 publications

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“…The melanoma images that are confirmed by the pathologist are treated as negative images, while the nevus ones are treated as positive images. For instance, the classifying of breast tissue by [18] found 67.1% accuracy in labeling nuclei as benign or malignant, the classification of histopathology images presented in [28] resulted in 88.7% accuracy in the diagnosis of lung cancer, 94.9% accuracy in the typing of malignant mesothelioma, and 80.0% to 82.9% accuracy in the prognosis of malignant mesothelioma for Fulgent stained lung sections. Keenan et al [13] reported accuracies of 62.3%-76.5% in the grading of H&E stained cervical tissue.…”

Section: Resultsmentioning

confidence: 99%

A fast BEMD algorithm based on multi-scale extrema

2015

Information Technology

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Abstract-Severe melanoma is potentially life-threatening. A novel methodology for automatic feature extraction from histopathological images and subsequent classification is presented. The proposed automated system uses a number of features extracted from images of skin lesions through image processing techniques which consisted of a spatially winner and adaptive median filter then applied Gabor filter bank to improve diagnostic accuracy. Histogram equalization to enhance the contrast of the images prior to segmentation is used. Then, a wavelet approach is used to extract the features; more specifically Wavelet Packet Transform (WPT).This article introduces a novel melanoma detection strategy using a hybrid particle swarm -based support vector machine (SVM-WLG -PSO) technique. The extracted features are reduced by using a particle swarm optimization (PSO), this was used to optimize the SVM parameters as a feature selection and finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The obtained classification accuracies show better performance in comparison to similar approaches for feature extraction. The proposed system is able to achieve one of the best results with classification accuracy of 87.13%, sensitivity of 94.1% and specificity of 80.22%.Keywords-Lesion, histo-pathological images, winner, adaptive median filter (AMF), Gabor filter bank, Histogram Equalization, particle swarm optimization (PSO), support vector machine (SVM).

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Section: Resultsmentioning

confidence: 99%

A fast BEMD algorithm based on multi-scale extrema

2015

Information Technology

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“…A bag of words model used effectively in text retrieval is further extended as Bag-of-visual-words for image classification and retrieval [14] [20]. Researchers have taken various approaches for representing an image with the bag-of-words model.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Technique Based on N-GRAM and Neural Networks for Classification of Mammographic Images

Kulkarni

Stranieri

Kulkarni

et al. 2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Various texture, shape, boundary features have been used previously to classify regions of interest in radiological mammograms into normal and abnormal categories. Although, bag-ofphrases or n-gram model has been effective in text representation for classification or retrieval of text, these approaches have not been widely explored for medical image processing. Our purpose is to represent regions of interest using an n-gram model, then deploy the n-gram features into a back-propagation trained neural network for classifying regions of interest into normal and abnormal categories. Experiments on the benchmark miniMIAS database show that

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“…A bag of words model used effectively in text retrieval is further extended as Bag-of-visual-words for image classification and retrieval [33] [39]. Researchers have taken various approaches for representing an image with the bag-of-words model.…”

Section: Literature Reviewmentioning

confidence: 99%