Shape and Texture Features Extraction Using Segmented Histopathological Images

2021 ◽  
pp. 907-913
Author(s):  
U. Rajyalakshmi ◽  
K. Satya Prasad ◽  
S. Koteswara Rao
Keyword(s):  
Texture Features ◽  
Features Extraction ◽  
Histopathological Images

scholarly journals Signature Texture Features Extraction Using GLCM Approach in Android Studio

2021 ◽  
Vol 1804 (1) ◽  
pp. 012043
Author(s):  
Hussein Awad Dwaich ◽  
Huda Abdulaali Abdulbaqi
Keyword(s):  
Texture Features ◽  
Features Extraction

scholarly journals A Comparative Evaluation of Texture Features for Semantic Segmentation of Breast Histopathological Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 64331-64346
Author(s):  
R. Rashmi ◽  
Keerthana Prasad ◽  
Chethana Babu K. Udupa ◽  
V. Shwetha
Keyword(s):  
Comparative Evaluation ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Histopathological Images

Texture Features Extraction of Chest HRCT Images Based on Granular Computing

2010 ◽  
Vol 5 (6) ◽  
Author(s):  
Gang Xie ◽  
Tianrui Cao ◽  
Chengdong Yan ◽  
Zhifeng Wu
Keyword(s):  
Granular Computing ◽  
Texture Features ◽  
Features Extraction

scholarly journals Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2031 ◽  
Author(s):  
Taimoor Shakeel Sheikh ◽  
Yonghee Lee ◽  
Migyung Cho
Keyword(s):  
State Of The Art ◽  
Texture Features ◽  
Feature Maps ◽  
Histopathological Classification ◽  
Multi Scale ◽  
Machine Learning Methods ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Histopathological Images

Diagnosis of pathologies using histopathological images can be time-consuming when many images with different magnification levels need to be analyzed. State-of-the-art computer vision and machine learning methods can help automate the diagnostic pathology workflow and thus reduce the analysis time. Automated systems can also be more efficient and accurate, and can increase the objectivity of diagnosis by reducing operator variability. We propose a multi-scale input and multi-feature network (MSI-MFNet) model, which can learn the overall structures and texture features of different scale tissues by fusing multi-resolution hierarchical feature maps from the network’s dense connectivity structure. The MSI-MFNet predicts the probability of a disease on the patch and image levels. We evaluated the performance of our proposed model on two public benchmark datasets. Furthermore, through ablation studies of the model, we found that multi-scale input and multi-feature maps play an important role in improving the performance of the model. Our proposed model outperformed the existing state-of-the-art models by demonstrating better accuracy, sensitivity, and specificity.

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