Automated marker identification using the Radon transform for watershed segmentation

González-Betancourt, Aniel; Rodríguez-Ribalta, Patricia; Meneses‐Marcel, Alfredo; Rodríguez, Sergio Sifontes; Lorenzo-Ginori, Juan V.; Orozco-Morales, Rubén

doi:10.1049/iet-ipr.2016.0525

Cited by 11 publications

(7 citation statements)

References 21 publications

(25 reference statements)

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“…A median filter has been found to be effective for reducing impulse noises from the microscopic images, even though recent studies have shown that geometric mean filter provides better performance than the median filter [ 12 , 45 ]. However, morphological operators have been greatly used with successful performances, imposing themselves as powerful alternatives to more common and used techniques for image enhancement and noise filtering [ 10 , 14 , 24 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 37 , 38 , 39 , 51 ].…”

Section: Discussionmentioning

confidence: 99%

“…In [ 27 ], Gonzalez-Betancourt et al proposed a system to determine markers for watershed segmentation based on the Radon transform and mathematical operators. In the first step of the process, small irrelevant structures and part of the noise are eliminated by a morphological filter, in order to ensure the preservation of the cells edges.…”

Section: Computer Aided Diagnosis Of Malaria By Using Mathematicalmentioning

confidence: 99%

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Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

Loddo

Ruberto

Kocher

2018

Sensors

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Abstract:Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis. Nevertheless, this kind of visual inspection is subjective, error-prone and time-consuming. In order to overcome the issues, numerous methods of automatic malaria diagnosis have been proposed so far. In particular, many researchers have used mathematical morphology as a powerful tool for computer aided malaria detection and classification. Mathematical morphology is not only a theory for the analysis of spatial structures, but also a very powerful technique widely used for image processing purposes and employed successfully in biomedical image analysis, especially in preprocessing and segmentation tasks. Microscopic image analysis and particularly malaria detection and classification can greatly benefit from the use of morphological operators. The aim of this paper is to present a review of recent mathematical morphology based methods for malaria parasite detection and identification in stained blood smears images.

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Section: Discussionmentioning

confidence: 99%

Section: Computer Aided Diagnosis Of Malaria By Using Mathematicalmentioning

confidence: 99%

Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

Loddo

Ruberto

Kocher

2018

Sensors

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“…Next, the ability of the Radon transform to detect circle-shaped objects is exploited to split clustered sEV 46–49 . As a general idea, the numerical value of each specific pixel in the sinogram represents the projection of all the pixels in the image along a line determined by its slope and position.…”

Section: Methodsmentioning

confidence: 99%

Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images

Gómez-de-Mariscal

Maška

Kotrbová

et al. 2019

Sci Rep

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Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30–200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.

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“…To summarize, the number and location of foreground markers contribute to the performance of watershed segmentation. In a study, Radon transform was employed to improve the selection of markers [ 12 ]. However, they only succeeded to improve efficiency on running time.…”

Section: Introductionmentioning

confidence: 99%

Selective Hole Filling of Red Blood Cells for Improved Marker-Controlled Watershed Segmentation

Nurçin¹,

Imanov²

2021

Scanning

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Manual counting and evaluation of red blood cells with the presence of malaria parasites is a tiresome, time-consuming process that can be altered by environmental conditions and human error. Many algorithms were presented to segment red blood cells for subsequent parasitemia evaluation by machine learning algorithms. However, the segmentation of overlapping red blood cells always has been a challenge. Marker-controlled watershed segmentation is one of the methods that was implemented to separate overlapping red blood cells. However, a high number of overlapped red blood cells were still an issue. We propose a novel approach to improve the segmentation efficiency of marker-controlled watershed segmentation. Local minimum histogram background segmentation with a selective hole filling algorithm was introduced to improve segmentation efficiency of marker-controlled watershed segmentation on a high number of overlapping red blood cells. The local minimum was selected on the smoothed histogram for background segmentation. The combination of selective filling, convex hull, and Hough circle detection algorithms was utilized for the intact segmentation of red blood cells. The markers were computed from the resulted mask, and finally, marker-controlled watershed segmentation was applied to separate overlapping red blood cells. As a result, the proposed algorithm achieved higher background segmentation accuracy compared to popular background segmentation algorithms, and the inclusion of corner details improved watershed segmentation efficiency.

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Automated marker identification using the Radon transform for watershed segmentation

Cited by 11 publications

References 21 publications

Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images

Selective Hole Filling of Red Blood Cells for Improved Marker-Controlled Watershed Segmentation

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