Gleason grade-based automatic classification of prostate cancer pathological images

2011 ◽  
Author(s):  
Ali Almuntashri ◽  
Sos Agaian ◽  
Ian Thompson ◽  
Danny Rabah ◽  
Osman Zin Al-Abdin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Automatic Classification ◽  
Gleason Grade

scholarly journals Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images

2015 ◽  
Vol 112 (46) ◽  
pp. E6265-E6273 ◽  
Author(s):  
Duc Fehr ◽  
Harini Veeraraghavan ◽  
Andreas Wibmer ◽  
Tatsuo Gondo ◽  
Kazuhiro Matsumoto ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Texture Features ◽  
Disease Status ◽  
Automatic Classification ◽  
Magnetic Resonance Images ◽  
Separate Analysis ◽  
Cancer Aggressiveness ◽  
Augmentation Techniques ◽  
Radiological Image

Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination of disease status at diagnosis without subjecting patients to surgical biopsies. We present machine learning-based automatic classification of prostate cancer aggressiveness by combining apparent diffusion coefficient (ADC) and T2-weighted (T2-w) MRI-based texture features. Our approach achieved reasonably accurate classification of Gleason scores (GS) 6(3+3) vs. ≥7 and 7(3+4) vs. 7(4+3) despite the presence of highly unbalanced samples by using two different sample augmentation techniques followed by feature selection-based classification. Our method distinguished between GS 6(3+3) and ≥7 cancers with 93% accuracy for cancers occurring in both peripheral (PZ) and transition (TZ) zones and 92% for cancers occurring in the PZ alone. Our approach distinguished the GS 7(3+4) from GS 7(4+3) with 92% accuracy for cancers occurring in both the PZ and TZ and with 93% for cancers occurring in the PZ alone. In comparison, a classifier using only the ADC mean achieved a top accuracy of 58% for distinguishing GS 6(3+3) vs. GS ≥7 for cancers occurring in PZ and TZ and 63% for cancers occurring in PZ alone. The same classifier achieved an accuracy of 59% for distinguishing GS 7(3+4) from GS 7(4+3) occurring in the PZ and TZ and 60% for cancers occurring in PZ alone. Separate analysis of the cancers occurring in TZ alone was not performed owing to the limited number of samples. Our results suggest that texture features derived from ADC and T2-w MRI together with sample augmentation can help to obtain reasonably accurate classification of Gleason patterns.

Interpreting HVEM of muscle-cell impulse networks

1992 ◽  
Vol 50 (1) ◽  
pp. 126-127
Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn
Keyword(s):  
Structural Analysis ◽  
Muscle Cell ◽  
Electron Micrographs ◽  
Relative Size ◽  
Automatic Classification ◽  
Nerve Fibers ◽  
Analysis Algorithm ◽  
Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

805: High-Level Cyclin D1 is Associated with High Gleason Grade and Predicts Worse Biochemical-Free Survival in Prostate Cancer

2006 ◽  
Vol 175 (4S) ◽  
pp. 260-260
Author(s):  
Rile Li ◽  
Hong Dai ◽  
Thomas M. Wheeler ◽  
Anna Frolov ◽  
Gustavo Ayala
Keyword(s):  
Prostate Cancer ◽  
Cyclin D1 ◽  
Gleason Grade ◽  
Free Survival ◽  
High Level

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

An embedded system for automatic classification of neonatal cry

2013 ◽  
Author(s):  
Biswanath Saha ◽  
Parimal Kumar Purkait ◽  
Jayanta Mukherjee ◽  
Arun Kumar Majumdar ◽  
Bandana Majumdar ◽  
...  
Keyword(s):  
Embedded System ◽  
Automatic Classification
Sign in / Sign up

Export Citation Format

Share Document