Literature Review
Volume 4 Issue 6 - 2020
Malaria Parasite Detection-An Approach
Shawni Dutta1 and Samir Kumar Bandyopadhyay2
1Lecturer, Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India
2Academic Advisor, The Bhawanipur Education Society College, Kolkata, India
*Corresponding Author: Shawni Dutta, Lecturer, Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India.
Received: April 27, 2020; Published: May 20, 2020




Abstract

Malaria has been a well-researched domain over the last 2 decades. While clinical research pertaining to curing of Malaria has been majorly conclusive, detection of malaria parasite in digitized thin blood smear image still stands open for investigation. Manual investigation of malaria parasite in thin blood smear image stands tedious and is liable to be affected by human bias. Herein lies the need for an automated CAD system. The proposed system is aimed to serve as a trustworthy aid to pathologists in malaria parasite detection and subsequent stage/species interpretation. The system has two broad phases, namely, the initial screening phase and main phase. The initial screening phase uses 40X magnification based images and is a precursor decision making phase to estimate whether the concerned digital image contains infection or not. If the image contains infection, the image is further investigated at 100X magnification in the main phase. The main phase encapsulates the infection detection phase along with stage/specie classification. So, the main phase is an independent, standalone phase in itself tailored as per WHO specification for malaria parasite detection. The main phase extends the initial screening phase by incorporating texture features for stage/specie of infection prediction. The initial screening phase was tested on 250 images (MaMic database) that were handpicked by the consultant pathologist. The main phase was built and evaluated on two datasets, namely, the MaMic and Government Hospital dataset (total 2730 images) using 10 fold cross validation to prevent overfitting of the data model. The prediction Accuracy, Sensitivity and Specificity of the main phase was recorded as 0.9917, 0.9948, and 0.9892 respectively using an Adaboost Classifier. Both phases use a strategic amalgamation of rule based and machine learning algorithms. The algorithm developed works on variedly stained, digitized thin blood smear slides. The performance of the algorithms developed was comparable with the state of the art methods in terms of prediction infallibility and time complexity. Cost benefit analysis for the proposed system revealed enhanced profitable in terms of time, labour and money as opposed to the standard practice of diagnostic laboratories.

Image processing and image analysis is a core area of application in computer science. Several research work has contributed and consolidated this domain of research. With the progression and advancement of digital imaging, contribution towards meaningful assessment of images have gained importance. An image captured by some imaging modalities needs to be understood and deciphered to gain insight on the image/s and hence extract pertinent information from the captured image/s. Images can be captured using digital camera, scanners, telescopes, microscopes and other electro-optical devices used in different industries and domains. Similarly, images are also generated by medical imaging hardware like Computed Tomography Scanners (CT-Scans), Magnetic Resonance Imaging (MRI) machines, X-ray machines and digital Whole Slide Imaging Scanners (WSI).

The images obtained require processing so that meaningful information can be retrieved and collated. Computerized algorithms play a significant role to achieve this goal. Manual interpretation of image may vary based on the perception of details and reasoning capability of the operator. Human bias and fatigue are two key elements that impede the performance of manual recognition and interpretation of images. However, the advent of computerized image processing tools has helped to overcome these impediments by assisting the operators in image analysis.

Computerized Image processing or Digital image processing is often referred to as Computer Vision and found its initial application in the study of satellite imagery captured by spy satellites to track enemy movement. Though initial applications of Computer Vision and Pattern Analysis was in defence related research but now this has found applications in every walks of life. Digital image processing have wide range of application ranging from industrial manufacturing, Geographical Information System (GIS) and also in Medical imaging. Digital image processing and automated analysis of images have significantly contributed to the betterment of several processes that affect human lives.

This research work particularly contributes towards medical image processing. Medical images are generated by several imaging modalities. Such images are interpreted and analyzed by some medical professional. Based on the source and imaging modalities the professional may be a radiologist (in case of X-rays, CT, MRI imaging modalities) or a pathologist (microscopic).

Keywords: Image Processing; Malaria; Thin Blood; GIS

Citation: Shawni Dutta and Samir Kumar Bandyopadhyay. “Malaria Parasite Detection-An Approach”. EC Emergency Medicine and Critical Care 4.6 (2020): 98-102.

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