Classification of hematologic malignancies using texton signatures

Abstract

We describe a decision support system to distinguish among hematology cases directly from microscopic specimens. The system uses an image database containing digitized specimens from normal and four different hematologic malignancies. Initially, the nuclei and cytoplasmic components of the specimens are segmented using a robust color gradient vector flow active contour model. Using a few cell images from each class, the basic texture elements (textons) for the nuclei and cytoplasm are learned, and the cells are represented through texton histograms. We propose to use support vector machines on the texton histogram based cell representation and achieve major improvement over the commonly used classification methods in texture research. Experiments with 3,691 cell images from 105 patients which originated from four different hospitals indicate more than 84% classification performance for individual cells and 89% for case based classification for the five class problem.

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

“…Many other methods collect features from either the original RGB image, a converted image to other non-biologically based color spaces (e.g., Lab or HSL), or from a grayscale version of that same image (Al-Kadi, 2010;Basavanhally et al, 2010;Dundar et al, 2011Dundar et al, , 2010Esgiar et al, 2002;Farjam et al, 2007;Glotsos et al, 2008;Huang and Lee, 2009;Jafari-Khouzani and SoltanianZadeh, 2003;Kong et al, 2009;Ozolek et al, 2014;Petushi et al, 2006;Qureshi et al, 2008;Ruiz et al, 2007;Schnorrenberg et al, 1997;Tabesh et al, 2005;Tabesh and Teverovskiy, 2006;Tahir and Bouridane, 2006;Thiran and Macq, 1996;Tuzel et al, 2007;Wang et al, 2010;Wetzel et al, 1999;Weyn et al, 1998;Xu et al 2014aXu et al , 2014b. Since hematoxylin binds to nucleic acids and eosin binds to protein, unmixing the stains allows the feature extraction to directly probe the state of these important biological molecules, whereas features from the mixed image may either miss this signal or be unable to probe them independently.…”

Theoretical and experimental comparison of different approaches for color texture classification

“…In industry, a common use is related to sorting products into lots of similar visual appearance, [9][10][11] a problem usually referred to as surface grading. Medicine is another field where classification comes into its own: breast 12 and skin 13 cancer detection, histopathological image analysis, 14 classification of endoscopic images, 15,16 and detection of haematological malignancies 17 are just some examples of possible applications. Other fields of application include (but are not limited to) analysis and classification of industrially prepared foods, 18,19 terrain 20, 21 and rock 22 classification, seabed characterization, 23 and crowd monitoring.…”

Multiple Classifier Systems in Texton-Based Approach for the Classification of CT Images of Lung

“…In some computer vision applications, it has been shown that building textons on the output of filter banks produces better accuracy [23,24]. Although using raw pixel representation is computationally more attractive than using filter bank representation (as the intermediate step of convolving patches with the filter banks is not required), using filter banks in texton-based approach will be investigated in the future work for possible improvement of the results.…”