Fundamental Technology for Computer-aided Diagnosis. 1. Gray-level Operation and Geometric Operation

2008 ◽  
Vol 64 (4) ◽  
pp. 463-472
Author(s):  
Yuji Ogata
Keyword(s):  
Computer Aided Diagnosis ◽  
Gray Level ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Geometric Operation

KLASIFIKASI TUMOR JINAK DAN TUMOR GANAS PADA CITRA MAMMOGRAM MENGGUNAKAN GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN SUPPORT VECTOR MACHINE (SVM)

2021 ◽  
Vol 26 (2) ◽  
pp. 176-186
Author(s):  
Lulu Mawaddah Wisudawati
Keyword(s):  
Support Vector Machine ◽  
World Health Organization ◽  
Computer Aided Diagnosis ◽  
World Health ◽  
Support Vector ◽  
Gray Level ◽  
Computer Aided ◽  
Health Organization ◽  
Occurrence Matrix ◽  
Aided Diagnosis

Kanker payudara merupakan penyebab utama kematian pada wanita. Data Global Cancer Observatory 2018 dari World Health Organization (WHO, 2020) menunjukkan kasus kanker yang paling banyak terjadi di Indonesia adalah kanker payudara, yakni 58.256 kasus atau 16.7% dari total 348.809 kasus kanker. Mamografi merupakan teknik yang paling umum digunakan dalam mendeteksi tumor payudara menggunakan sistem sinar-X dosis rendah. Ada beberapa tipe abnormalitas dalam citra mammogram, yaitu mikrokalsifikasi dan massa. Penelitian ini bertujuan untuk meningkatkan performa sistem Computer-Aided Diagnosis (CAD) dalam mengklasifikasi tumor jinak dan tumor ganas dengan mengembangkan metode ekstraksi fitur menggunakan Gray Level Co-Occurrence Matrix (GLCM) dan metode klasifikasi menggunakan Support Vector Machine (SVM). Uji coba dilakukan dengan menggunakan database DDSM dengan 256 citra abnormal (95 tumor jinak dan 161 tumor ganas) menghasilkan nilai akurasi sebesar 83.59% dengan nilai sensitivitas dan spesifisitas 87.58% dan 76.84%. Selain itu, didapatkan nilai AUC sebesar 0.98%. Metode tersebut menunjukkan bahwa sistem memberikan hasil performa yang baik dalam mengklasifikasi tumor jinak dan tumor ganas.

scholarly journals Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Khin Yadanar Win ◽  
Somsak Choomchuay ◽  
Kazuhiko Hamamoto ◽  
Manasanan Raveesunthornkiat ◽  
Likit Rangsirattanakul ◽  
...  
Keyword(s):  
Pleural Effusion ◽  
Cancer Cells ◽  
Texture Features ◽  
Computer Aided Diagnosis ◽  
Classification Model ◽  
Morphological Operations ◽  
Gray Level ◽  
Cell Nuclei ◽  
Computer Aided ◽  
Aided Diagnosis

Cytological screening plays a vital role in the diagnosis of cancer from the microscope slides of pleural effusion specimens. However, this manual screening method is subjective and time-intensive and it suffers from inter- and intra-observer variations. In this study, we propose a novel Computer Aided Diagnosis (CAD) system for the detection of cancer cells in cytological pleural effusion (CPE) images. Firstly, intensity adjustment and median filtering methods were applied to improve image quality. Cell nuclei were extracted through a hybrid segmentation method based on the fusion of Simple Linear Iterative Clustering (SLIC) superpixels and K-Means clustering. A series of morphological operations were utilized to correct segmented nuclei boundaries and eliminate any false findings. A combination of shape analysis and contour concavity analysis was carried out to detect and split any overlapped nuclei into individual ones. After the cell nuclei were accurately delineated, we extracted 14 morphometric features, 6 colorimetric features, and 181 texture features from each nucleus. The texture features were derived from a combination of color components based first order statistics, gray level cooccurrence matrix and gray level run-length matrix. A novel hybrid feature selection method based on simulated annealing combined with an artificial neural network (SA-ANN) was developed to select the most discriminant and biologically interpretable features. An ensemble classifier of bagged decision trees was utilized as the classification model for differentiating cells into either benign or malignant using the selected features. The experiment was carried out on 125 CPE images containing more than 10500 cells. The proposed method achieved sensitivity of 87.97%, specificity of 99.40%, accuracy of 98.70%, and F-score of 87.79%.

Application of Fuzzy Enhancement in the Diagnosis of Liver Cancer from Ultrasound Images

2012 ◽  
Vol 195-196 ◽  
pp. 493-497
Author(s):  
Wu Qiu ◽  
Feng Xiao ◽  
Xin Yang ◽  
Mao Lin Ye ◽  
Yu Chi Ming ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Back Propagation ◽  
Normal Liver ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Ultrasound Images ◽  
Gray Level ◽  
Fuzzy Enhancement ◽  
Computer Aided ◽  
Aided Diagnosis

Fuzzy enhancement is applied in computer aided diagnosis of liver cancer from B mode ultrasound images as a pre-processing procedure in this paper. It was evaluated with three classifiers including K means, back propagation neural network and support vector machine using 25 features from single gray-level statistic, gray-level co-occurrence matrix (GLCM), and gray-level run-length matrix (GLRLM). The results show that the fuzzy enhancement algorithm can improve classification accuracy of normal liver, liver cancer and Hemangioma from B mode ultrasound images for three classifiers. It is proved that fuzzy enhancement as an efficient preprocessing procedure could be used in the computer aided diagnosis system of liver cancer.

Pattern-Recognition: A Comparison of the Performance of Clinicians and Non-Clinicians - with a Note on the Performance of a Computer-Based System

1972 ◽  
Vol 11 (01) ◽  
pp. 32-37 ◽  
Author(s):  
F. T. DE DOMBAL ◽  
J. C. HORROCKS ◽  
J. R. STANILAND ◽  
P. J. GUILLOU
Keyword(s):  
Pattern Recognition ◽  
Computer System ◽  
Computer Aided Diagnosis ◽  
Clinical Interview ◽  
Computer Performance ◽  
Pattern Size ◽  
Computer Aided ◽  
Computer Based ◽  
Aided Diagnosis ◽  
Better Than

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.

COMPUTER-AIDED DIAGNOSIS OF GASTRIC LESIONS USING MAGNIFYING NARROW BAND IMAGING ENDOSCOPY

2019 ◽  
Author(s):  
S Kashin ◽  
R Kuvaev ◽  
E Kraynova ◽  
H Edelsbrunner ◽  
O Dunaeva ◽  
...  
Keyword(s):  
Narrow Band ◽  
Narrow Band Imaging ◽  
Computer Aided Diagnosis ◽  
Gastric Lesions ◽  
Computer Aided ◽  
Aided Diagnosis
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