Analysis of Image Segmentation Algorithms for the Effective Detection of Leukemic Cells

T, Bhagya; Anand, K.; Kanchana, D.; S, Remya Ajai A

doi:10.1109/icoei.2019.8862696

Cited by 14 publications

(5 citation statements)

References 11 publications

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“…Setelah memperoleh 150 dataset citra tanah pasir berdasarkan pemotretan langsung dilokasi penelitian, selanjutnya citra tersebut diolah menggunakan metode Otsu Thresholding. Analisis metode Otsu Thresholding merupakan metode umum untuk menghasilkan hasil yang baik, sehingga dapat digunakan untuk menentukan tahapan segmentasi butiran tanah pasir [21], hal ini juga mengacu kepada penelitian Bhagya T, dkk [22]. Hasil analisis algoritma otsu thresholding, kemudian diimplementasikan kedalam bahasa pemrograman matlab.…”

Section: A Dataunclassified

Penerapan Pengolahan Citra Tanah Pasir Untuk Mengukur Kekuatan Tanah Sebagai Dasar Pertimbangan Konstruksi Bangunan Atau Jalan Menggunakan Metode Otsu Thresholding

Tomasila,

Tamtelahitu,

Palyama

2023

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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Citra tanah pasir yang digunakan bersifat digital yaitu citra dapat langsung disimpan dalam media penyimpanan (komputer), berupa angka-angka yang menunjukkan besar intensitas pada masing-masing piksel. Atau digambarkan sebagai sebuah fungsi f(x,y) dua dimensi, dimana x merupakan baris dan y merupakan kolom atau dapat juga disimbolkan dengan M x N dimana M adalah baris dan N adalah kolom. Koordinat x dan y atau M dan N menunjukan lokasi piksel. Selain itu citra digital dapat dikatakan sebagai sekumpulan piksel (elemen terkecil dari suatu citra) [19]. Berikut merupakan gambaran posisi suatu piksel pada citra seperti tampak pada gambar 4:

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Section: A Dataunclassified

Penerapan Pengolahan Citra Tanah Pasir Untuk Mengukur Kekuatan Tanah Sebagai Dasar Pertimbangan Konstruksi Bangunan Atau Jalan Menggunakan Metode Otsu Thresholding

Tomasila,

Tamtelahitu,

Palyama

2023

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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“…The analysis of the two methods is the Watershed Transform method and the Otsu Thresholding method to determine the stages of segmentation with reference to the research of (Li, Zhang, et al, 2019) and (Bhagya et al, 2019). Following are the steps for implementing the watershed transform and otsu thresholding methods.…”

Section: Figure 2 Flow Chart Of Research Stepsmentioning

confidence: 99%

Sand Soil Image Processing Using the Watershed Transform and Otsu Thresholding Based on Gaussian Noise

Tomasila

2022

jinav j. inf. vis.

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Image processing technology is one of the technologies that can help facilitate and speed up human work, especially in the process of determining the grain size distribution of soil in a civil building plan. Its utilization has been widely used to study, analyze, and understand the structure and framework of the soil. Image analysis is carried out as an initial or fundamental step in image processing to discover and comprehend information. With so many image segmentation methods, it is necessary to conduct research to determine which method is best for sandy soil image segmentation based on one of the image segmentation quality criteria, namely gaussian image noise. By testing the watershed transform method and the Otsu thresholding method as two of the area-based methods that are considered suitable for segmenting sandy soil images before and after distorted Gaussian noise based on the calculation of the mean square error (MSE) value.The results showed that the watershed transform method is better for segmenting sandy soil images when compared to the Otsu thresholding method. This is indicated by the average squared error (mse) of 3.08 for the watershed transform method and 4.09 for the Otsu Thresholding method. In addition to the comparison of quality tests of sandy soil based on gaussian noise with standard deviation values of normal distribution and noise intensities of 10, 20, and 30, it proves that the watershed transform method is still better at segmenting noise-distorted sandy soil images than the Otsu thresholding method. However, in terms of processing time, the Otsu Thresholding method is faster or better than the Watershed Transform method. of the results or conclusions brief. There are no citations, tables or figures in abstract.

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“…1) Coarse Segmentation: Fluorescent staining can eliminate the interference of surrounding non-cell impurities, marking the cell area as a green fluorescent signal, and the rest background is marked as black (as shown in the second row of Figure 1). Therefore, in the coarse segmentation stage, the fluorescence-stained image is subjected to the maximum inter-class variance method (OTSU algorithm) [15], [16] to determine the maximum variance between the cell area and the background in the cell image. As a threshold value, the binarization operation is performed on each pixel in the image.…”

Section: B Cmfmentioning

confidence: 99%

Fused 3-Stage Image Segmentation for Pleural Effusion Cell Clusters

Zhao

Wang

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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The appearance of tumor cell clusters in pleural effusion is usually a vital sign of cancer metastasis. Segmentation, as an indispensable basis, is of crucial importance for diagnosing, chemical treatment, and prognosis in patients. However, accurate segmentation of unstained cell clusters containing more detailed features than the fluorescent staining images remains to be a challenging problem due to the complex background and the unclear boundary. Therefore, in this paper, we propose a fused 3-stage image segmentation algorithm, namely Coarse segmentation-Mapping-Fine segmentation (CMF) to achieve unstained cell clusters from whole slide images. Firstly, we establish a tumor cell cluster dataset consisting of 107 sets of images, with each set containing one unstained image, one stained image, and one ground-truth image. Then, according to the features of the unstained and stained cell clusters, we propose a three-stage segmentation method: 1) Coarse segmentation on stained images to extract suspicious cell regions-Region of Interest (ROI); 2) Mapping this ROI to the corresponding unstained image to get the ROI of the unstained image (UI-ROI); 3) Fine Segmentation using improved automatic fuzzy clustering framework (AFCF) on the UI-ROI to get precise cell cluster boundaries. Experimental results on 107 sets of images demonstrate that the proposed algorithm can achieve better performance on unstained cell clusters with an F1 score of 90.40%.

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Analysis of Image Segmentation Algorithms for the Effective Detection of Leukemic Cells

Cited by 14 publications

References 11 publications

Penerapan Pengolahan Citra Tanah Pasir Untuk Mengukur Kekuatan Tanah Sebagai Dasar Pertimbangan Konstruksi Bangunan Atau Jalan Menggunakan Metode Otsu Thresholding

Penerapan Pengolahan Citra Tanah Pasir Untuk Mengukur Kekuatan Tanah Sebagai Dasar Pertimbangan Konstruksi Bangunan Atau Jalan Menggunakan Metode Otsu Thresholding

Sand Soil Image Processing Using the Watershed Transform and Otsu Thresholding Based on Gaussian Noise

Fused 3-Stage Image Segmentation for Pleural Effusion Cell Clusters

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