Blood image analysis to detect malaria using filtering image edges and classification

Abstract

Malaria is a most dangerous mosquito borne disease and its infection spread through the infected mosquito. It especially affects the pregnant females and Children less than 5 years age. Malarial species commonly occur in five different shapes, Therefore, to avoid this crucial disease the contemporary researchers have proposed image analysis based solutions to mitigate this death causing disease. In this work, we propose diagnosis algorithm for malaria which is implemented for testing and evaluation in Matlab. We use Filtering and classification along with median filter and SVM classifier. Our proposed method identifies the infected cells from rest of blood images. The Median filtering smoothing technique is used to remove the noise. The feature vectors have been proposed to find out the abnormalities in blood cells. Feature vectors include (Form factor, measurement of roundness, shape, count total number of red cells and parasites). Primary aim of this research is to diagnose malaria by finding out infected cells. However, many techniques and algorithm have been implemented in this field using image processing but accuracy is not up to the point. Our proposed algorithm got more efficient results along with high accuracy as compared to NCC and Fuzzy classifier used by the researchers recently.

Utilizing Image Analysis with Machine Learning and Deep Learning to Identify Malaria Parasites in Conventional Microscopic Blood Smear Images

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Coronal slice segmentation using a watershed method for early identification of people with Alzheimer's

“…Widodo [24] applies the support vector machine method in recognizing lung disease. Memon [25] applied the Median filter to remove noise in the microscopic image. Supriyanti [26] implemented the histogram equalization method to normalize input images.…”

Halftoning-based BTC image reconstruction using patch processing with border constraint

“…Both of the ODBTC and EDBTC schemes yield better image quality compared to that of the classical BTC method as reported in [1,2]. The two methods can be applied to other image processing and computer vision applications, including low computational image compression [1,2,11], content-based image retrieval [7][8][9][10], recognition of color building [14,15], blood image analysis [16], object detection and tracking [17], etc. Although the ODBTC and EDBTC significantly reduce the blocking effect and false contour issues occurred in classical BTC technique, the impulsive noise is always present at considerably high level.…”