An Efficient Algorithm for Automatic Malaria Detection in Microscopic Blood Images

Somasekar, J.; Reddy, A. Rama Mohan; Reddy, L. Sreenivasulu

doi:10.1007/978-3-642-29216-3_47

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…The principle of this network is to transform the input using nonlinear transformation 'Ø'. Somasekar et al (2012) states that the multilayer perception network with back propagation gives a better result than Levenberg-Marquardt and Bayesian Rule algorithms in malaria parasite detection mechanism.…”

Section: Some Popular Methodologiesmentioning

confidence: 99%

Vision-based malaria parasite image analysis: a systematic review

Pattanaik

Swarnkar

2019

IJBRA

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Background: Malaria is one of the classic neglected serious diseases in many developing countries. The early stage of disease detection, accurate parasite count, detection of the aggressiveness of the disease, technical limitations, lack of expertise in malaria diagnosis and smart tools, lack of good quality healthcare services, funds so on are the challenges found during malaria diagnosis that requires a deeper analysis. Objectives: This paper aims to give a review of the automated diagnosis or visual inspection of malaria parasites using histology images of thin or thick blood film smears. Methods and Results: Various computer-aided diagnosis techniques are in use to solve tasks meticulously in a stratified description paradigm using non-linear transformation architectures. Conclusion: This work elaborates a comprehensive study of various computer vision diagnostic approaches already proposed in this field with a future direction for better quicker malaria identification.

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Section: Some Popular Methodologiesmentioning

confidence: 99%

Vision-based malaria parasite image analysis: a systematic review

Pattanaik

Swarnkar

2019

IJBRA

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“…The sensitivity achieved by the proposed technique is 85.5%. In the work of Somasekar, Reddy, Reddy, Krishna, and Babu (), the experiments are performed on the standard dataset DPDx. Only segmentation of parasites has been performed by arithmetic mean of the maximum and minimum intensity level.…”

Section: Segmentation (Parasites Infected or Non‐infected Rbcs)mentioning

confidence: 99%

Microscopic malaria parasitemia diagnosis and grading on benchmark datasets

Rehman

Abbas

Saba

et al. 2018

Microscopy Res & Technique

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Malaria parasitemia diagnosis and grading is hard and still far from perfection. Inaccurate diagnosis and grading has caused tremendous deaths rate particularly in young children worldwide. The current research deeply reviews automated malaria parasitemia diagnosis and grading in thin blood smear digital images through image analysis and computer vision based techniques. Actually, state-of-the-art reveals that current proposed practices present partially or morphology dependent solutions to the problem of computer vision based microscopy diagnosis of malaria parasitemia. Accordingly, a deep appraisal of the current practices is investigated, compared and analyzed on benchmark datasets. The open gaps are highlighted and the future directions are laid down for a complete automated microscopy diagnosis for malaria parasitemia based on those factors that have not been affected by other diseases. Moreover, a general computer vision framework to perform malaria parasitemia estimation/grading is constructed in universal directions. Finally, remaining problems are highlighted and possible directions are suggested. RESEARCH HIGHLIGHTS: The current research presents a microscopic malaria parasitemia diagnosis and grading of malaria in thin blood smear digital images through image analysis and computer vision based techniques. The open gaps are highlighted and future directions for a complete automated microscopy diagnosis of malaria parasitemia mentioned.

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“…There are four image repair methods used for comparisons such as contrast stretching, histogram equalization, low pass filter and Gaussian filtering. Research of [1] [2][3] [4] [5] used the median filter as an image repair method for malaria parasites identification. Research of [6] [7] used local histogram equalization.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Image Improvement Method on Parasite Images of Malaria

Rosnelly¹,

Wahyuni²

2018

JAIS

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Improved image is a process on the image that initially has a quality that is less good or has noise. In this image improvement operation image quality will be improved so that the image produces better quality. Image improvement methods used are contrast stretching, histogram equalization, low pass filter and Gaussian filtering. In this study compare contrast stretching method, histogram equalization, low pass filter and Gaussian filtering to improve image quality. Performance of each method would be calculated by finding the value of Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). This study compares contrast stretching methods, histogram equalization, low pass filter and Gaussian filtering to improve image quality. Total data of malaria parasite image is 120. The data consist of image of malaria parasite falciparum, vivax, malariae along with stage that is ring, tropozoit, skizon and gametocyte. Evaluate the performance of each method by finding Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) values. The result is a contrast stretching provides better image quality against malaria parasite image.

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An Efficient Algorithm for Automatic Malaria Detection in Microscopic Blood Images

Cited by 8 publications

References 13 publications

Vision-based malaria parasite image analysis: a systematic review

Vision-based malaria parasite image analysis: a systematic review

Microscopic malaria parasitemia diagnosis and grading on benchmark datasets

Comparison of Image Improvement Method on Parasite Images of Malaria

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