Automated and unsupervised detection of malarial parasites in microscopic images

<p>Abstract</p> <p>Background</p> <p>Malaria is a serious infectious disease. According to the World Health Organization, it is responsible for nearly one million deaths each year. There are various techniques to diagnose malaria of which manual microscopy is considered to be the gold standard. However due to the number of steps required in manual assessment, this diagnostic method is time consuming (leading to late diagnosis) and prone to human error (leading to erroneous diagnosis), even in experienced hands. The focus of this study is to develop a robust, unsupervised and sensitive malaria screening technique with low material cost and one that has an advantage over other techniques in that it minimizes human reliance and is, therefore, more consistent in applying diagnostic criteria.</p> <p>Method</p> <p>A method based on digital image processing of Giemsa-stained thin smear image is developed to facilitate the diagnostic process. The diagnosis procedure is divided into two parts; enumeration and identification. The image-based method presented here is designed to automate the process of enumeration and identification; with the main advantage being its ability to carry out the diagnosis in an unsupervised manner and yet have high sensitivity and thus reducing cases of false negatives.</p> <p>Results</p> <p>The image based method is tested over more than 500 images from two independent laboratories. The aim is to distinguish between positive and negative cases of malaria using thin smear blood slide images. Due to the unsupervised nature of method it requires minimal human intervention thus speeding up the whole process of diagnosis. Overall sensitivity to capture cases of malaria is 100% and specificity ranges from 50-88% for all species of malaria parasites.</p> <p>Conclusion</p> <p>Image based screening method will speed up the whole process of diagnosis and is more advantageous over laboratory procedures that are prone to errors and where pathological expertise is minimal. Further this method provides a consistent and robust way of generating the parasite clearance curves.</p

Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional

Occluded face recognition based on Gabor wavelets

A new feature based approach to frontal face recognition with Gabor wavelets is presented in this paper. The feature points are automatically extracted using the local characteristics of each individual face in order to decrease the effect of occluded features. There is no training as in neural network approaches, thus single frontal face for each individual is enough as reference. Experimental results show that the proposed method achieves a recognition ratio of over %95

GAYE: A face recognition system

In this paper, a new face recognition system, GAYE, is presented. GAYE is a fully automatic system that detects and recognizes faces in cluttered scenes. The input of the system is any digitized image/image sequence that includes face/faces. The basic building blocks of the system are face detection, feature extraction and feature comparison. Face detection is based on skin color segmentation. For feature extraction, a novel approach is proposed that depends on the Gabor wavelet transform of the face image. By comparing facial feature vectors system finally makes a decision if the incoming person is recognized or not. Real time system tests show that GAYE achieves a recognition ratio over %90

Performance of a malaria microscopy image analysis slide reading device.

BACKGROUND: Viewing Plasmodium in Romanovsky-stained blood has long been considered the gold standard for diagnosis and a cornerstone in management of the disease. This method however, requires a subjective evaluation by trained, experienced diagnosticians and establishing proficiency of diagnosis is fraught with many challenges. Reported here is an evaluation of a diagnostic system (a "device" consisting of a microscope, a scanner, and a computer algorithm) that evaluates scanned images of standard Giemsa-stained slides and reports species and parasitaemia. METHODS: The device was challenged with two independent tests: a 55 slide, expert slide reading test the composition of which has been published by the World Health Organization ("WHO55" test), and a second test in which slides were made from a sample of consenting subjects participating in a malaria incidence survey conducted in Equatorial Guinea (EGMIS test). These subjects' blood was tested by malaria RDT as well as having the blood smear diagnosis unequivocally determined by a worldwide panel of a minimum of six reference microscopists. Only slides with unequivocal microscopic diagnoses were used for the device challenge, n = 119. RESULTS: On the WHO55 test, the device scored a "Level 4" using the WHO published grading scheme. Broken down by more traditional analysis parameters this result was translated to 89% and 70% sensitivity and specificity, respectively. Species were correctly identified in 61% of the slides and the quantification of parasites fell within acceptable range of the validated parasitaemia in 10% of the cases. On the EGMIS test it scored 100% and 94% sensitivity/specificity, with 64% of the species correct and 45% of the parasitaemia within an acceptable range. A pooled analysis of the 174 slides used for both tests resulted in an overall 92% sensitivity and 90% specificity with 61% species and 19% quantifications correct. CONCLUSIONS: In its current manifestation, the device performs at a level comparable to that of many human slide readers. Because its use requires minimal additional equipment and it uses standard stained slides as starting material, its widespread adoption may eliminate the current uncertainty about the quality of microscopic diagnoses worldwide