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Computer assisted classification framework for prediction of acute lymphoblastic and acute myeloblastic leukemia

Rawat J., Singh A., Bhadauria H. S., Virmani J., Devgun J. S.

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 4

637--654

Hematological malignancies i.e. acute lymphoid leukemia and acute myeloid leukemia are the types of blood cancer that can affect blood, bone marrow, lymphatic system and are the major contributors to cancer deaths. In present work, an attempt has been made to design a CAC (computer aided classification system) for diagnosis of myeloid and lymphoid cells and their FAB (French, American, and British) characterization. The proposed technique improves the AML and ALL diagnostic accuracy by analyzing color, morphological and textural features from the blood image using image processing and to assist in the development of a computer-aided screening of AML and ALL. This paper endeavors at proposing a quantitative microscopic approach toward the discrimination of malignant from normal in stained blood smear. The proposed technique firstly segments the nucleus from the leukocyte cell background and then computes features for each segmented nucleus. A total of 331 geometrical, chromatic and texture features are computed. A genetic algorithm using support vector machine (SVM) classifier is used to optimize the feature space. Based on optimized feature space, an SVM classifier with various kernel functions is used to eradicate noisy objects like overlapped cells, stain fragments, and other kinds of background noises. The significance of the proposed method is tested using 331 features on 420 microscopic blood images acquired from the online repository provided by the American society of hematology. The results confirmed the viability or potential of using a computer aided classification method to reinstate the monotonous and the reader-dependent diagnostic methods.

Estimation of copper concentrate grade based on color features and least-squares support vector regression

Ren C., Yang J., Liang C.

Physicochemical Problems of Mineral Processing

2015

Vol. 51, iss. 1

163--172

In this paper, a new method based on color features of microscopic image and least-squares support vector regression model (LS-SVR) is proposed for indirect measurement of copper concentrate grade. Red, green and blue (RGB), hue and color vector angle were extracted from color microscopic images of a copper concentrate sample and selected for the comparison. Three different estimation models based on LS-SVR were developed using RGB, hue, and color vector angle, respectively. A comparison of three models was carried out through a validation test. The best model was obtained for the hue giving a running time of 30.243 ms, root mean square error of 0.8644 and correlation coefficient value of 0.9997. The results indicated that the copper concentrate grade could be estimated by the LS-SVR model using the hue as input parameter with a satisfactory accuracy.