Devkumar Das

Automated leukocyte recognition using fuzzy divergence.

This paper aims at introducing an automated approach to leukocyte recognition using fuzzy divergence and modified thresholding techniques. The recognition is done through the segmentation of nuclei where Gamma, Gaussian and Cauchy type of fuzzy membership functions are studied for the image pixels. It is in fact found that Cauchy leads better segmentation as compared to others. In addition, image thresholding is modified for better recognition. Results are studied and discussed.

Computational microscopic imaging for malaria parasite detection: a systematic review

Malaria, being an epidemic disease, demands its rapid and accurate diagnosis for proper intervention. Microscopic image‐based characterization of erythrocytes plays an integral role in screening of malaria parasites. In practice, microscopic evaluation of blood smear image is the gold standard for malaria diagnosis; where the pathologist visually examines the stained slide under the light microscope. This visual inspection is subjective, error‐prone and time consuming. In order to address such issues, computational microscopic imaging methods have been given importance in recent times in the field of digital pathology. Recently, such quantitative microscopic techniques have rapidly evolved for abnormal erythrocyte detection, segmentation and semi/fully automated classification by minimizing such diagnostic errors for computerized malaria detection. The aim of this paper is to present a review on enhancement, segmentation, microscopic feature extraction and computer‐aided classification for malaria parasite detection.

Statistical pattern analysis of white blood cell nuclei morphometry

Quantitative microscopy has strengthened conventional diagnostic scheme through better understanding of microscopic features from clinical perspective. Towards this, pathological image analysis has gained immense significance among medical fraternity through visualization and quantitative evaluation of clinical features. Till today pathological inspection of human blood is solely dependent on subjective assessment which usually leads to significant inter-observer variation in grading and subsequently resulting in late diagnosis of certain disease. This paper introduced a systematic approach to morphologically characterize five types of white blood cells (WBC) through statistical pattern analytics. Marker controlled watershed segmentation embedded with morphological operator is employed to segment WBC and its nuclei from light microscopic image of blood samples. Henceforth, one cellular and eight nuclei-based geometric features are computed mathematically and analyzed statistically with t-test and kernel density functions to show their discriminating potentiality among the groups. Amongst all these features, only four statistical significant features are fed to Naïve Bayes classifier for pattern identification with 83.2% overall accuracy. Detailed results are also given here.

Invariant moment based feature analysis for abnormal erythrocyte recognition

Erythrocyte shape recognition is very important in the detection of thalassemia and anemia using microscopic images. This study aims to develop a computer aided shape recognizer for the recognition of abnormal shapes viz., tear drop, echinocyte, eliptocyte. Here such recognition is done using Hus moments and other geometric features followed by gray level thresholding and marker controlled watershed segmentation. These features are statistically evaluated to show their significant in discriminating the mentioned abnormal and normal shapes. In the result, it is found that six moment based features are significant.

Quantitative microscopy approach for shape-based erythrocytes characterization in anaemia

Anaemia is one of the most common diseases in the world population. Primarily anaemia is identified based on haemoglobin level; and then microscopically examination of peripheral blood smear is required for characterizing and confirmation of anaemic stages. In conventional approach, experts visually characterize abnormality present in the erythrocytes under light microscope, and this evaluation process is subjective in nature and error prone. In this study, we have proposed a methodology using machine learning techniques for characterizing erythrocytes in anaemia associated with anaemia using microscopic images of peripheral blood smears. First, peripheral blood smear images are preprocessed based on grey world assumption technique and geometric mean filter for reducing unevenness of background illumination and noise reduction. Then erythrocyte cells are segmented using marker‐controlled watershed segmentation technique. The erythrocytes in anaemia, such as, tear drop, echinocyte, acanthocyte, elliptocyte, sickle cells and normal erythrocytes cells have been characterized and classified based on their morphological changes. Optimal subset of features, ranked by information gain measure provides highest classification performance using logistic regression classifier in comparison with other standard classifiers.

Automated system for characterization and classification of malaria‐infected stages using light microscopic images of thin blood smears

In this paper, we propose a comprehensive image characterization cum classification framework for malaria‐infected stage detection using microscopic images of thin blood smears. The methodology mainly includes microscopic imaging of Leishman stained blood slides, noise reduction and illumination correction, erythrocyte segmentation, feature selection followed by machine classification. Amongst three‐image segmentation algorithms (namely, rule‐based, Chan–Vese‐based and marker‐controlled watershed methods), marker‐controlled watershed technique provides better boundary detection of erythrocytes specially in overlapping situations. Microscopic features at intensity, texture and morphology levels are extracted to discriminate infected and noninfected erythrocytes. In order to achieve subgroup of potential features, feature selection techniques, namely, F‐statistic and information gain criteria are considered here for ranking. Finally, five different classifiers, namely, Naive Bayes, multilayer perceptron neural network, logistic regression, classification and regression tree (CART), RBF neural network have been trained and tested by 888 erythrocytes (infected and noninfected) for each features’ subset. Performance evaluation of the proposed methodology shows that multilayer perceptron network provides higher accuracy for malaria‐infected erythrocytes recognition and infected stage classification. Results show that top 90 features ranked by F‐statistic (specificity: 98.64%, sensitivity: 100%, PPV: 99.73% and overall accuracy: 96.84%) and top 60 features ranked by information gain provides better results (specificity: 97.29%, sensitivity: 100%, PPV: 99.46% and overall accuracy: 96.73%) for malaria‐infected stage classification.

Probabilistic prediction of malaria using morphological and textural information

The objective of this paper is to introduce a computer assisted prediction of malaria infection particularly Plasmodium vivax based on the morphological and textural information. Here erythrocytes have been segmented from light microscopic images of peripheral blood smear using marker controlled watershed followed by pre-processing. Thereafter texture and morphology of erythrocytes are extracted using geometrical and Haralick texture measure. Finally statistically significant features are fitted with multivariate regression model. This predictive model is good fitted (−2 Log Likelihood = 24.636 and Chi-Square = 820.949) and it gives 88.77 % prediction accuracy.

Plasmodium vivax segmentation using modified fuzzy divergence

This paper aims at introducing a new approach to Plasmodium vivax (P. vivax) detection from Leishman stained thin blood film. This scheme follows retrospective study design protocol where patients were selected at random in the clinic. The scheme consists of two main stages - firstly artefacts reduction, and secondly fuzzy divergence based segmentation of P. vivax infected region(s) from erythrocytes. Here, malaria parasite segmentation is done using divergence based threshold selection. Fuzzy approach is chosen to minimize ambiguity inherent in the microscopic images. Divergence algorithm is derived from Cauchy membership function to overcome the drawbacks in comparison with other well known membership functions.

Entropy based divergence for leukocyte image segmentation

This work aims to develop the divergence measures based on Renyis and Yagers entropies for segmenting the leukocyte nuclei from microscopic image of peripheral blood smear. Such measure minimizes the separation between the actual and ideal thresholded image. Finally, these measures have been compared with Shannon entropy based divergence algorithm. In fact, it is observed here that Yagers measure provides better result in segmenting the leukocyte nuclei from the background of the image. The effectiveness of our proposed methods is demonstrated on blood cytopathological images of normal and chronic myelogenous leukemia (CML) samples.

Quantitative characterisation of Plasmodium vivax in infected erythrocytes: a textural approach

This paper aims at introducing a textural pattern analysis approach to Plasmodium vivax P. vivax detection from Leishman stained thin blood film. This scheme follows retrospective study design protocol where patients were selected at random in the clinic. The scheme consists of four stages - artefacts reduction, fuzzy divergence-based segmentation of P. vivax infected regions and normal erythrocytes, textural feature extraction using grey level co-occurrence matrix and fractal dimension, finally classification. Here, we have extracted seven features, out of which five are statistically significant in discriminating textures between malaria and normal classes based on light microscopic blood images at 100× resolutions. Finally, Bayesian and support vector machine-based classifiers are trained and validated with 100 cases and 100 control subjects. In effect, it is hereby observed that the significant textural features lead to discriminate P. vivax with 95% and 98% accuracies for SVM and Bayesian classifiers respectively. Results are studied and compared.