Multi-Scale Spatially Weighted Local Histograms in O(1)

Poostchi, Mahdieh; Shafiekhani, Ali; Palaniappan, Kannappan; Seetharaman, Guna

doi:10.1109/aipr.2017.8457944

Cited by 1 publication

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References 27 publications

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“…Different techniques have been proposed including Otsu thresholding 12 , 38 – 40 and watershed algorithms 41 – 43 that are usually combined with morphology operations to improve segmentation results and address texture complexities; however, improper clump splitting and over-segmentation are the main drawbacks of the methods based on histogram thresholding and watershed transform 7 . To address the splitting of overlapping cells and avoid over segmentation marker-controlled watershed algorithms, 25 , 44 , 45 template matching, 32 , 46 Ada-boost, 31 distance transform, 47 and active contour models 21 , 48 – 50 have been presented, which perform poor to segment highly overlapping cells.…”

Section: Automatic Detection and Segmentation Of Red Blood Cellsmentioning

confidence: 99%

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy

et al. 2018

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Abstract.Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright–Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.

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