Classification of microcalcifications in digitised mammograms using multiscale statistical texture analysis

2002 ◽  
Author(s):  
D. Kramer ◽  
F. Aghdasi
Keyword(s):  
Texture Analysis

scholarly journals 3D texture analysis for classification of second harmonic generation images of human ovarian cancer

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Bruce Wen ◽  
Kirby R. Campbell ◽  
Karissa Tilbury ◽  
Oleg Nadiarnykh ◽  
Molly A. Brewer ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Second Harmonic Generation ◽  
Texture Analysis ◽  
Harmonic Generation ◽  
Second Harmonic ◽  
Human Ovarian Cancer ◽  
3D Texture ◽  
3D Texture Analysis

scholarly journals Mechanical Fault Classification of an Induction Motor using Texture Analysis

2013 ◽  
Vol 18 (12) ◽  
pp. 11-19 ◽  
Author(s):  
Won-Chul Jang ◽  
Yong-Hoon Park ◽  
Myeong-Su Kang ◽  
Jong-Myon Kim
Keyword(s):  
Induction Motor ◽  
Texture Analysis ◽  
Fault Classification

Tumor Classification Based on Regional Heterogeneity Using Pixel Level Feature Descriptors

2021 ◽  
Vol 11 (5) ◽  
pp. 1481-1488
Author(s):  
C. Gunasundari ◽  
K. Selva Bhuvaneswari
Keyword(s):  
Texture Analysis ◽  
Early Stage ◽  
Pituitary Tumors ◽  
Brain Regions ◽  
Regional Heterogeneity ◽  
Gradient Based ◽  
Classification Of Tumors ◽  
Effective Diagnosis ◽  
The Brain

Brain tumor is considered to be widely analyzed disease for effective diagnosis and treatment planning. Several approaches were framed to detect and diagnose tumor at early stage. In this work, texture analysis is carried out to analyze the nature of tumor and categorize it. Around 3064 images were analyzed during this study consisting of meningioma, glioma and pituitary tumors. Intensity and gradient pixel based texture analysis is carried out in this analysis. Results confirm that the tumors can be classified and categorized based on the intensity and gradient pixel information. A total of 2216 feature vector is extracted it is observed that the gradient based information aids for better classification of tumors. Localized binary patterns are found to provide detailed information about the subtle variation in the brain regions due to the presence of abnormality in brain tissues. It is further observed that the normalized feature vectors show better differentiation between tumor categories. The ROC and PRC curves exhibit the high classification ability using the extracted features to differentiate tumor grades.

scholarly journals Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis

1994 ◽  
Vol 39 (12) ◽  
pp. 2273-2288 ◽  
Author(s):  
A Petrosian ◽  
Heang-Ping Chan ◽  
M A Helvie ◽  
M M Goodsitt ◽  
D D Adler
Keyword(s):  
Texture Analysis ◽  
Normal Tissue ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis
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