A robust segmentation method for counting bovine milk somatic cells in microscope slide images

Melo, Gabriel Jesus Alves de; Gomes, Viviani; Baccili, Camila Costa; Almeida, L.A.L. de; Lima, Carla Caroline Valença de

doi:10.1016/j.compag.2015.05.018

Cited by 9 publications

(8 citation statements)

References 42 publications

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“…In [40], a self-counting mechanism is proposed for flagellate trypanosomes infecting human cells: Firstly, morphological pre-treatment removes complex image backgrounds; secondly, unsupervised classification is used to segment collections; thirdly, threshold processing is used to preserve infected cells; and finally, cells are processed by morphological treatment and filtered by average. In [41], a robust segmentation method is proposed for counting milk somatic cells on a microscope slide image. By changing the original RGB(Red-Green-Blue, three primary colors) format of the image to the lab color space and applying the k-means clustering algorithm to remove debris and other background features, a new gray-level threshold processing method is proposed, and the remaining boundary units are separated in the final segmentation step of applying the watershed transform.…”

Section: Related Workmentioning

confidence: 99%

Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets

et al. 2019

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The isolation and observation of cardiomyocytes serve as the fundamental approach to cardiovascular research. The state-of-the-practice for the isolation and observation relies on manual operation of the entire culture process. Such a manual approach not only incurs high human errors, but also takes a long period of time. This paper proposes a new computer-aided paradigm to automatically, accurately, and efficiently perform the clustering and counting of cardiomyocytes, one of the key procedures for evaluating the success rate of cardiomyocytes isolation and the quality of culture medium. The key challenge of the proposed method lies in the unique, rod-like shape of cardiomyocytes, which has been hardly addressed in literature. Our proposed method employs a novel algorithm inspired by hesitant fuzzy sets and integrates an efficient implementation into the whole process of analyzing cardiomyocytes. The system, along with the data extracted from adult rats’ cardiomyocytes, has been experimentally evaluated with Matlab, showing promising results. The false accept rate (FAR) and the false reject rate (FRR) are as low as 1.46% and 1.97%, respectively. The accuracy rate is up to 98.7%—20% higher than the manual approach—and the processing time is reduced from tens of seconds to 3–5 s—an order of magnitude performance improvement.

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Section: Related Workmentioning

confidence: 99%

Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets

et al. 2019

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“…Some of the cells that remained connected during the use of global thresholding can be separated by the use of the watershed transform with 8-connected neighborhood [25] . Therefore, we applied the watershed algorithm to the R channel image [26] .…”

Section: Thresholdingmentioning

confidence: 99%

Lymphoma images analysis using morphological and non-morphological descriptors for classification

Nascimento

Martins²,

Tosta

et al. 2018

Computer Methods and Programs in Biomedicine

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Mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia are the principle subtypes of the non-Hodgkin lymphomas. The diversity of clinical presentations and the histopathological features have made diagnosis of such lymphomas a complex task for specialists. Computer aided diagnosis systems employ computational vision and image processing techniques, which contribute toward the detection, diagnosis and prognosis of digitised images of histological samples. Studies aimed at improving the understanding of morphological and non-morphological features for classification of lymphoma remain a challenge in this area. This work presents a new approach for the classification of information extracted from morphological and non-morphological features of nuclei of lymphoma images. We extracted data of the RGB model of the image and employed contrast limited adaptive histogram equalisation and 2D order-statistics filter methods to enhance the contrast and remove noise. The regions of interest were identified by the global thresholding method. The flood-fill and watershed techniques were used to remove the small false positive regions. The area, extent, perimeter, convex area, solidity, eccentricity, equivalent diameter, minor axis and major axis measurements were computed for the regions detected in the nuclei. In the feature selection stage, we applied the ANOVA, Ansari-Bradley and Wilcoxon rank sum methods. Finally, we employed the polynomial, support vector machine, random forest and decision tree classifiers in order to evaluate the performance of the proposed approach. The non-morphological features achieved higher AUC and AC values for all cases: the obtained rates were between 95% and 100%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the studied groups. The proposed approach is useful as an automated protocol for the diagnosis of lymphoma histological tissue.

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“…Many researchers have studied automated methods to identify and to count objects contained in digital images. Using clustering, thresholding and segmentation, Melo et al (2015) determined the number of somatic cells in cow's milk in microscope slide images for the detection of mastitis. The study by Kothari et al (2009) was used for cell groups of four different cancerous tissues in digital images.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated counting model for arbuscular mycorrhizal fungi spores using the Circle Hough Transform and an artificial neural network

Melo

Lopes

Andrade

et al. 2019

An. Acad. Bras. Ciênc.

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Arbuscular Mycorrhizae (AM) are mutualistic associations between Arbuscular Mycorrhizal Fungi (AMF) and the roots of many plant species. AMF spores give rise to filaments that develop in the root system of plants and contribute to the absorption of water and some nutrients. This article introduces a semi-automated counting model of AMF spores in slide images based on Artificial Neural Network (ANN). The semi-automated counting of AMF spores facilitates and accelerates the tasks of researchers, who still do the AMF spore counting manually. We built a representative database of spore images, processing images through the Circle Hough Transform (CHT) method and training an ANN to classify patterns automatically. The classification analysis and the performances of the proposed method against the manual method are presented in this paper. The accuracy for the identification of spores by CHT in conjunction to ANN classification in the images was 90%. The results indicate that this method can accurately detect the presence of AMF spores in images as well as count them with a high level of confidence.

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A robust segmentation method for counting bovine milk somatic cells in microscope slide images

Cited by 9 publications

References 42 publications

Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets

Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets

Lymphoma images analysis using morphological and non-morphological descriptors for classification

Semi-automated counting model for arbuscular mycorrhizal fungi spores using the Circle Hough Transform and an artificial neural network

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