Aryo Harto, Aryo
Teknik Informatika, Institut Teknologi Sepuluh Nopember

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SEGMENTASI DAN PEMISAHAN SEL DARAH PUTIH BERSENTUHAN MENGGUNAKAN K-MEANS DAN HIERARCHICAL CLUSTERING ANALYSIS PADA CITRA LEUKEMIA MYELOID AKUT Harto, Aryo; Fatichah, Chastine
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 15, No. 2, Juli 2017
Publisher : Teknik Informatika, ITS

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Abstract

The success of identification and classification on diagnosing acute myeloid leukemia (AML) diseases based on image processing relies heavily on segmentation result. Segmentation on peripheral blood smear images aims to separate the leukocytes region with others region. To increase the segmentation accuracy on AML images, a few things regarding lighting condition, contrast, staining variations and the existence of touching cells must be overcome. In this study a method for leukocytes segmentation and separate the touching cell on AML images using cluster analysis with K-Means and hierarchical clustering analysis (HCA) is proposed. K-Means method is used to analyze the cluster for AML images segmentation. The AML image datasets with various staining variations is segmented using K-Means method.  The existence of touching cells is separated using HCA method which produce a stable clusters result. Segmentation and cell separation will be processed on local region or sub-image which is obtained from AML images cropping. From the evaluation results in 40 images of AML dataset, the proposed method is capable to properly segment the white blood cells region and separating the touching cell into a single cells. The average value of the segmentation results is 0.977 for precision, 0.885 for recall and 0.928 for Zijdenbos similarity index (ZSI) in white blood cell region. While in nucleus region the average value is 0.975 for precision, 0.924 for recall and 0.948 for ZSI. On cell counting, the error rate is also low which about 7.68%.
CORTICAL BONE SEGMENTATION USING WATERSHED AND REGION MERGING BASED ON STATISTICAL FEATURES Hani`ah, Mamluatul; Aditya, Christian Sri Kusuma; Harto, Aryo; Arifin, Agus Zainal
Jurnal Ilmu Komputer dan Informasi Vol 8, No 2 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

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Abstract

Research on biomedical image is a subject that attracted many researchers??? interest. This is because the biomedical image could contain important information to help analyze a disease. One of the existing researches in his field uses dental panoramic radiographs image to detect osteoporosis. The analyzed area is the width of cortical bone. To analyze that area, however, we need to determine the width of the cortical bone. This requires proper segmentation on the dental panoramic radiographs image. This study proposed the integration of watershed and region merging method based on statistical features for cortical bone segmentation on dental panoramic radiographs. Watershed segmentation process was performed using gradient magnitude value from the input image. The watershed image that still has excess segmentation could be solved by region merging based on statistical features. Statistical features used in this study are mean, standard deviation, and variance. The similarity of adjacent regions is measured using weighted Euclidean distance from the statistical feature of the regions. Merging process was executed by incorporating the background regions as many as possible, while keeping the object regions from being merged. The segmentation result has succeeded in forming the contours of the cortical bone. The average value of accuracy is 93.211%, while the average value of sensitivity and specificity is 93.858% and respectively.