Analysis of Detecting the Malarial Parasite Infected Blood Images Using Statistical Based Approach

Raviraja, S.; Bajpai, Gaurav; Sharma, Sudhir Kumar

doi:10.1007/978-3-540-68017-8_126

Cited by 12 publications

(6 citation statements)

References 3 publications

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“…Malaria parasite in HSV (Hue, Saturation, and Value) color space was segmented by [13]. Erythrocytes infected by malaria parasites were detected by using statistical approach [14]. Mathematical morphology and granulometry approaches and gray level thresholding for estimation of parasitemia were applied [15,16].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of different types of Malaria Diseases using First Order Features

Ajala¹,

Funmilola²,

Fenwa³

et al. 2015

IJAIS

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Malaria is majorly caused by three parasitic organisms namely, P. malaria, P. vivax, P. falciparum. Physician (Microbiologists, Laboratory technicians, Medical Practitioners, and medical experts) examines erythrocytes under light microscope to study the color and morphological changes toward malaria diagnosis. Assessment accuracy depends on the physician-pathological understanding. To help physicians in cases where they may be wrong, developing a computer assisted malaria parasite detection tool has helped modern pathological services where a physician is able to get assistance in order to quickly make better decision toward malaria diagnosis. Segmentation of medical images helps in identifying features that are needed for clinical diagnosis. First order features always give accurate information about the medical image and useful for identification and analysis. In this work, first order features which entails the mean, median, mode, standard deviation, energy, Skewness, kurtosis, area, entropy were extracted from the infected malaria images. The corresponding results show a significant steady value which could be useful during classification of medical images. The work is ongoing, the aim of the work is to classify the malaria parasite and to come up with a diagnostic system that will be able to diagnose tissue and blood cell based diseases like malaria, Ebola etc.

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Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of different types of Malaria Diseases using First Order Features

Ajala¹,

Funmilola²,

Fenwa³

et al. 2015

IJAIS

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“…Some of the authors contributed the detection of malarial parasites based on Support Vector Machine and Neural network .The paper by Diaz et al [8] evaluates a color segmentation technique for separation of pixels into three different classes: parasite, red blood cell and background, based on standard supervised classification algorithms. Four different supervised classification techniques -KNN, Naive Bayes, SVM and Neural network -are evaluated on different color spaces -RGB, normalized RGB, HSV and YCbCr.…”

Section: Releted Workmentioning

confidence: 99%

“…In malarial blood pathologists look for red cells, white cell, parasites in different stages of life, immature and mature trophozoites, schizonts and gametocytes. It is difficult to differentiate between various stages, but S. Raviraja et al [8] contributed for identification of different stages based on color, shape and size. But due to use of statistical approach results are not up to the mark.…”

Section: Backroundmentioning

confidence: 99%

RBCs and Parasites Segmentation from Thin Smear Blood Cell Images

Panchbhai¹,

Damahe²,

Nagpure³

et al. 2012

IJIGSP

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Abstract-Manually examine the blood smear for the detection of malaria parasite consumes lot of time for trend pathologists. As the computational power increases, the role of automatic visual inspection becomes more important. An automated system is therefore needed to complete as much work as possible for the identification of malaria parasites. The given scheme based on used of RGB color space, G layer processing, and segmentation of Red Blood Cells (RBC) as well as cell parasites by auto-thresholding with offset value and use of morphological processing. The work compare with the manual results obtained from the pathology lab, based on total RBC count and cells parasite count. The designed system successfully detects malaria parasites and RBC cells in thin smear image.

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“…Amongst various epidemic diseases, malaria is one of the major health burdens worldwide with highest frequency in Africa and south East Asian countries. According to WHO report (Raviraja et al, 2007), malaria is endemic in total 104 countries where each year more than 2.7 billion people die as a result of it with maximum casualties being children. Malaria is the most common type of parasitic infection in Indian population.…”

Section: Introductionmentioning

confidence: 99%

“…Malaria is the most common type of parasitic infection in Indian population. It has the highest number of malaria patients in South-East Asia with an approximate 1.5-2.7 million deaths per annum (Raviraja et al, 2007). Plasmodium sp.…”

Section: Introductionmentioning

confidence: 99%

Computational microscopic imaging for malaria parasite detection: a systematic review

Das

Mukherjee

Chakraborty

2015

Journal of Microscopy

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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.

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Analysis of Detecting the Malarial Parasite Infected Blood Images Using Statistical Based Approach

Cited by 12 publications

References 3 publications

Comparative Analysis of different types of Malaria Diseases using First Order Features

Comparative Analysis of different types of Malaria Diseases using First Order Features

RBCs and Parasites Segmentation from Thin Smear Blood Cell Images

Computational microscopic imaging for malaria parasite detection: a systematic review

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