Automated system for malaria parasite identification

Savkare, S. S.; Narote, S. P.

doi:10.1109/iccict.2015.7045660

Cited by 49 publications

(35 citation statements)

References 10 publications

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“…To achieve noise elimination, Median filtering has been adapted by most authors like, Ruberto et al [16], Ross et al [17], Anggraini et al [18], Das et al [6], Sutkar et al [19], Chandra et al [20], Rosado et al [21], Predanan et al [22], Bahendwar et al [23] and Nugroho et al [24]. Authors Dave et al [25] and Savkare et al [26] have used a combination of Median filtering with Laplacian filter for noise removal along with enhancement of the edge region. Adaptive and local histogram equalisation method is used by Sio et al [27], Gaussian filter is employed by Arko et al [13] and SUSAN filter by Ahirwar et al [28] for noise reduction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Histogram thresholding is performed by authors Chandra et al [20] while author Preedanan et al [22] have adopted adaptive histogram thresholding for segmentation. The authors, Savkare et al [34], [26], Mehrjou [35], in there research citations have implemented Watershed transform with distance transform for segmentation of red blood cells. An alternative method of Marker controlled Watershed transform is performed by author Das et al [6] [37] and Khan et al [38].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Das et al [37], used texture based features with Multivariate Logistical Regression for identifying parasite in thin smear images. Authors Savkare et al [34], [26] implemented SVM (with colour, texture and shape features) for classification of parasite and normal red blood cells and Suryawanshi et al [39], used Euclidian Distance Classifier and SVM for classifying infected and non-infected cells. Similarly, Preedanan et al [22] used Statistical features from grayscale image and SVM with RBF kernel for classification, while Bairagi et al [32] employed Statistical and Texture based features with SVM classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The authors Ruberto et al [16], Tek et al [15], Das et al [6], Ahirwar et al [28], Prasad et al [33], Dave et al [25] and Savkare et al [26] have used Mathematical Morphology and/or Granulometry for determining the size of red blood cells/segmentation of foreground. The authors Reni et al [29] utilized a modified 'Morphological Closing' operation for artefact removal and preservation of maximum foreground information.…”

mentioning

confidence: 99%

“…Image binarization using Otsu thresholding for image segmentation was performed by authors Das et al [6], Savkare et al [34], Ahirwar et al [28], Anggraini et al [18]. Mehrjou [35], Malihi et al [36] Rosado et al [21] and Savkare et al [26]. Dave et al [25] have used grayscale histogram with Kurtosis (to determine uni/bimodal histogram) and then performed Otsu thresholding.…”

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confidence: 99%

See 4 more Smart Citations

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Bandyopadhyay¹,

Nag²,

Basu³

2018

Preprint

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Bandyopadhyay¹,

Nag²,

Basu³

2018

Preprint

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Extrinsic parameter's adjustment and potential implications in Plasmodium falciparum malaria diagnosis

Vijayalakshmi

2021

Microscopy Res & Technique

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Malaria is a major public health concern, affecting over 3.2 billion people in 91 countries. The advent of digital microscopy and Machine learning with the aim of automating Plasmodium falciparum diagnosis extensively depends on the extracted image features. The color of the cells, plasma, and stained artifacts influence the topological, geometrical, and statistical parameters being used to extract image features. During microscopic image acquisition, custom adjustments to the condenser and color temperature controls often have an influence on the extracted statistical features. But, our human visual system sub-consciously adjusts the color and retains the originality in a different lighting environment. Despite the use of appropriate image preprocessing, findings from the literature indicate that statistical feature variations exist, allowing the risk of P. falciparum misinterpretation. In order to eliminate this pervasive variation, the current work focuses on preprocessing the extracted statistical features rather than the prepossessing of the source image. It begins with the augmentation of series images for a microscopic field by inducing illumination variations during the microscopic image acquisition stage. A set of such image series is analyzed using a Nonlinear Regression Model to generalize the relationship between microscopic images acquired with variable ambient brightness and a specific feature.The projection point of the centroid feature onto the brightness parameter is identified in the model and it is denoted as the optimum brightness factor (OBF). Using the model, the feature correction factor (CF) is calculated from the rate of change of feature values over the interval OBF, and the brightness of the test image is processed.The present work has investigated OBF for selected image textural features, namely Contrast, Homogeneity, Entropy, Energy, and Correlation individually from its cooccurrence matrices. For performance analysis, the best state-of-the-art method uses selected texture as a subset feature to evaluate the effectiveness of P. falciparum malaria classification. Then, the impact of proposed feature processing is evaluated on 274 blood smear images with and without Feature Correction (FC). As a result, the "p" value is less than .05, which leads to the result that it is highly significant and the classification accuracy and F-score of P. falciparum malaria are increased.

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Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines

Gopakumar

Subrahmanyam

2019

Handbook of Deep Learning Applications

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Automated system for malaria parasite identification

Cited by 49 publications

References 10 publications

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Extrinsic parameter's adjustment and potential implications in Plasmodium falciparum malaria diagnosis

Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines

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