scholarly journals Automatic classification of colorectal and prostatic histologic tumor images using multiscale multispectral local binary pattern texture features and stacked generalization

2018 ◽  
Vol 275 ◽  
pp. 83-93 ◽  
Author(s):  
Rémy Peyret ◽  
Ahmed Bouridane ◽  
Fouad Khelifi ◽  
Muhammad Atif Tahir ◽  
Somaya Al-Maadeed
Keyword(s):  
Local Binary Pattern ◽  
Texture Features ◽  
Automatic Classification ◽  
Stacked Generalization

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.

Analysis of Microcalcifications Using Block Wise Local Binary Pattern and Independent component analysis in Mammograms

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Spandana Paramkusham ◽  
Dr. Kunda M.M. Rao ◽  
Dr. BVVSN Prabhakar Rao
Keyword(s):  
Breast Cancer ◽  
Independent Component Analysis ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Component Analysis ◽  
Independent Component ◽  
Significant Shift ◽  
Statistical Features ◽  
Calcium Deposits

In India, the average age of developing a breast cancer has undergone a significant shift over last few decades. Most prominent features that indicate breast cancer are microcalcifications. Microcalcifications are tiny calcium deposits deposited on skin and non-palpable. Automatic analysis of microcalcification helps specialist in having more precise decision. The paper presents an approach that involves classification of microcalcifications into benign/malignant in mammograms. Texture features such LBP and statistical features are extracted from ROIs with microcalcification and independent component analysis is applied to reduce the feature set. These feature set is fed to artificial neural networks to classify the ROIs into malignant and benign calcifications.

Automatic Classification of Wood Texture Using Local Binary Pattern & Fuzzy K-Nearest Neighbor

2014 ◽  
Vol 903 ◽  
pp. 315-320
Author(s):  
Ismail Mohd Khairuddin ◽  
Ali Abuassal ◽  
Ali Abdelrahim ◽  
Amar Faiz Zainal Abidin ◽  
Syahrul Hisham Mohamad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Local Binary Pattern ◽  
Nearest Neighbor ◽  
Automatic Classification ◽  
Previous Literature ◽  
K Nearest Neighbor

The price of the wood according to the type of wood. Classification of the woods can be done by studying its texture. This paper introduces Fuzzy k Nearest Neighbor to classify 25 types of wood. The woods images have been taken from the Wood Database of the Centre for Artificial Intelligence & Robotics, Universiti Teknologi Malaysia. The features of wood images are extracted using Local Binary Pattern. The results of this paper shows improvement in wood classification compare to the previous literature.

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.

scholarly journals Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy

2017 ◽  
Vol 38 ◽  
pp. 104-116 ◽  
Author(s):  
Sunhua Wan ◽  
Hsiang-Chieh Lee ◽  
Xiaolei Huang ◽  
Ting Xu ◽  
Tao Xu ◽  
...  
Keyword(s):  
Breast Tissue ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Optical Coherence ◽  
Optical Coherence Microscopy

scholarly journals A comparative study of auroral morphology distribution between the Northern and Southern Hemisphere based on automatic classification

2018 ◽  
Vol 7 (1) ◽  
pp. 113-122 ◽  
Author(s):  
Qiuju Yang ◽  
Ze-Jun Hu
Keyword(s):  
Comparative Study ◽  
Southern Hemisphere ◽  
Local Binary Pattern ◽  
Yellow River ◽  
Hot Spot ◽  
Morphological Characteristics ◽  
Automatic Classification ◽  
Geophysical Phenomenon ◽  
Comparative Results

Abstract. Aurora is a very important geophysical phenomenon in the high latitudes of Arctic and Antarctic regions, and it is important to make a comparative study of the auroral morphology between the two hemispheres. Based on the morphological characteristics of the four labeled dayside discrete auroral types (auroral arc, drapery corona, radial corona and hot-spot aurora) on the 8001 dayside auroral images at the Chinese Arctic Yellow River Station in 2003, and by extracting the local binary pattern (LBP) features and using a k-nearest classifier, this paper performs an automatic classification of the 65 361 auroral images of the Chinese Arctic Yellow River Station during 2004–2009 and the 39 335 auroral images of the South Pole Station between 2003 and 2005. Finally, it obtains the occurrence distribution of the dayside auroral morphology in the Northern and Southern Hemisphere. The statistical results indicate that the four dayside discrete auroral types present a similar occurrence distribution between the two stations. To the best of our knowledge, we are the first to report statistical comparative results of dayside auroral morphology distribution between the Northern and Southern Hemisphere.

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