scholarly journals Specific Changes of Chromatin Structure in Nuclei of Normal Epithelium Adjacent to Laryngeal Squamous Cell Carcinoma

2000 ◽  
Vol 20 (2-3) ◽  
pp. 141-150 ◽  
Author(s):  
Thomas Dreyer ◽  
Iris Knoblauch ◽  
David Garner ◽  
Alexei Doudkine ◽  
Calum MacAulay ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Texture Feature ◽  
Texture Features ◽  
Discriminant Functions ◽  
Paraffin Sections ◽  
Normal Epithelium ◽  
Integrated Optical ◽  
Nuclear Texture

The aim of this study was to confirm the existence of specific nuclear texture feature alterations of histologically normal epithelial borders nearby invasive laryngeal cancer (NC).Paraffin sections of NC and of chronic inflammations unrelated to cancer (CI) were analysed for nuclear texture and for integrated optical density (IOD‐index) and were compared to normal epithelium of patients without evidence of cancer (NE). Several discriminant functions based on nuclear texture features were trained to separate different subgroups.As the most important result, specific nuclear texture feature shifts were only found in NC with high‐density lymphocytic stroma infiltrate (NC+). Classification of nuclei of NE versus NC+ was correct in 70%. The same classifier was correct in only 58% when nuclei of NE were classified versus CI. We also found lower values of IOD‐Index within the NC+ group when compared to NE (p< 0:001).

scholarly journals Hydrolysis Profiles of Formalin Fixed Paraffin-Embedded Tumors Based on IOD (Integrated Optical Density) and Nuclear Texture Feature Measurements

1999 ◽  
Vol 19 (3-4) ◽  
pp. 111-118
Author(s):  
Margareta Fležar ◽  
Marija Us‐Krašovec ◽  
Mario žganec ◽  
Jaka Lavrenčak ◽  
Rastko Golouh
Keyword(s):  
Texture Feature ◽  
Texture Features ◽  
Serous Carcinoma ◽  
Hydrolysis Time ◽  
Formalin Fixed Paraffin ◽  
Integrated Optical ◽  
Formalin Fixed Paraffin Embedded ◽  
Serous Tumor ◽  
Borderline Malignancy ◽  
Nuclear Texture

The aim of the study was to determine optimal hydrolysis time for the Feulgen DNA staining of archival formalin fixed paraffin‐embedded surgical samples, prepared as single cell suspensions for image cytometric measurements. The nuclear texture features along with the IOD (integrated optical density) of the tumor nuclei were analysed by an automated high resolution image cytometer as a function of duration of hydrolysis treatment (in 5 N HCl at room temperature). Tissue blocks of breast carcinoma, ovarian serous carcinoma, ovarian serous tumor of borderline malignancy and leiomyosarcoma were included in the study. IOD hydrolysis profiles showed plateau between 30 and 60 min in the breast carcinoma and leiomyosarcoma, and between 40 and 60 min in the ovarian serous carcinoma and ovarian serous tumor of borderline malignancy. Most of the nuclear texture features remained stable after 20 min of hydrolysis treatment. Our results indicate that the optimal hydrolysis time for IOD and for nuclear texture feature measurements, was between 40 and 60 min in the cell preparations from tissue blocks of three epithelial and one soft tissue tumor.

scholarly journals Classification of Computer Graphic Images and Photographic Images Based on Fusion of Color and Texture Features

2021 ◽  
Vol 35 (3) ◽  
pp. 201-207
Author(s):  
Halaguru Basavarajappa Basanth Kumar ◽  
Haranahalli Rajanna Chennamma
Keyword(s):  
Computer Graphic ◽  
Texture Feature ◽  
Texture Features ◽  
Digital Cameras ◽  
Global Features ◽  
Photographic Images ◽  
Occurrence Matrix ◽  
Paper Classification ◽  
Color And Texture Features

With the rapid advancement in digital image rendering techniques, allows the user to create surrealistic computer graphic (CG) images which are hard to distinguish from photographs captured by digital cameras. In this paper, classification of CG images and photographic (PG) images based on fusion of global features is presented. Color and texture of an image represents global features. Texture feature descriptors such as gray level co-occurrence matrix (GLCM) and local binary pattern (LBP) are considered. Different combinations of these global features are investigated on various datasets. Experimental results show that, fusion of color and texture features subset can achieve best classification results over other feature combinations.

scholarly journals Classification of thyroid follicular lesions based on nuclear texture features-Lesion size matters

2010 ◽  
Vol 77A (12) ◽  
pp. 1101-1102 ◽  
Author(s):  
Konradin Metze ◽  
Rita C. Ferreira ◽  
Randall L. Adam
Keyword(s):  
Texture Features ◽  
Lesion Size ◽  
Follicular Lesions ◽  
Nuclear Texture

scholarly journals UNSUPERVISED TEXTURE CLASSIFICATION OF ENTROPY BASED LOCAL DESCRIPTOR USING K-MEANS CLUSTERING ALGORITHM

2011 ◽  
pp. 133-140 ◽  
Author(s):  
S. S. Sreeja Mole ◽  
L. Ganesan
Keyword(s):  
Texture Analysis ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Texture Classification ◽  
Clustering Algorithms ◽  
Texture Feature ◽  
Texture Features ◽  
Local Descriptor ◽  
Descriptor Approach

This paper presents an efficient approach for unsupervised Texture Segmentation and Classification, based on features extracted from entropy based local descriptor using K-means clustering with spatial information. The K- means clustering algorithm is commonly used in computer vision as a form of image segmentation. Texture analysis refers to a class of mathematical procedures and models that characterizes the spatial variations within imagery as a means of extracting information. Texture analysis may require the solution of two different problems first is Segmentation and Classification of a given image according to the different texture and second was for of a given texture with respect to a set of known textures. Based on the proposed concept, this paper describes the entropy based local descriptor using K-Means with spatial information approach. Experimental results show that the proposed framework performs very well compared to other clustering algorithms in all measured criteria. Spatial information has been effectively used for unsupervised texture classification for Brodatz of texture images. The model is not specifically confined to a particular texture feature. We tested this algorithm using other texture features. The proposed entropy based local descriptor approach gives good accuracy when compared with other methods.

Texture Feature Extraction and Classification of Pork Loin Ultrasonography Images

2013 ◽  
Vol 791-793 ◽  
pp. 1978-1981
Author(s):  
Tao Li ◽  
Jian Xun Zhang ◽  
Quan Sun
Keyword(s):  
Feature Extraction ◽  
Ultrasound Image ◽  
Texture Feature ◽  
Texture Features ◽  
Fat Content ◽  
Image Processing Algorithm ◽  
Texture Feature Extraction ◽  
Pork Loin ◽  
Pork Fat

The method of texture feature extraction and classification of pork loin B ultrasound image is proposed, which can be applied to the computer-aided judgment the pork loin fat content of pork loin. 5 texture features which is based on the texture of the co-occurrence matrix are extracted from the B ultrasound image of pork loin according to the digital image processing algorithm. Using the correlation analysis method to select the key texture extraction in the first step. Then,the classification is realized based on the BP neural network. The train set and test set are randomly chosen from 135 cases. Tests performed show that the proposed method result in a high classification accuracy, which will provide the researcher a valuable opinion on the pork fat content detection.

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.

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