Image Analysis and Mathematical Morphology.

Biometrics ◽  
1983 ◽  
Vol 39 (2) ◽  
pp. 536 ◽  
Author(s):  
P. J. Diggle ◽  
J. Serra
Keyword(s):  
Image Analysis ◽  
Mathematical Morphology

DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY

2013 ◽  
Vol 25 (03) ◽  
pp. 1350029 ◽  
Author(s):  
Baljit Singh Khehra ◽  
Amar Partap Singh Pharwaha
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
Early Detection ◽  
Contrast Enhancement ◽  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Mammogram Image ◽  
Background Contrast

Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background. Main advantage of Kapur measure of entropy over Shannon entropy is that Kapur measure of entropy has α and β parameters that can be used as adjustable values. These parameters can play an important role as tuning parameters in the image processing chain for the same class of images. Experiments have been conducted on images of mini-Mammogram Image Analysis Society (MIAS) database (UK). Experiment results of the proposed approach are compared with histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) and fuzzy histogram hyperbolization (FHH) which are well-established image enhancement techniques. In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and dense-glandular) of mini-MIAS database are considered. Objective image quality assessment parameters: Target-to-background contrast enhancement measurement based on standard deviation (TBCSD), target-to-background contrast enhancement measurement based on entropy (TBCE), contrast improvement index (CII), peak signal-to-noise ratio (PSNR) and average signal-to-noise ratio (ASNR) are used to evaluate the performance of proposed approach. The experimental results show that the proposed approach performs well. This study can be a part of developing a computer-aided diagnosis (CAD) system for early detection of breast cancer.

Mathematical morphology-based imaging of gastrointestinal cancer cell motility and 5-aminolevulinic acid-induced fluorescence

2019 ◽  
Vol 64 (6) ◽  
pp. 711-720
Author(s):  
Rima Ramonaite ◽  
Robertas Petrolis ◽  
Simge Unay ◽  
Gediminas Kiudelis ◽  
Jurgita Skieceviciene ◽  
...  
Keyword(s):  
Image Analysis ◽  
Lymph Node ◽  
Cell Motility ◽  
Mathematical Morphology ◽  
Cancer Cell ◽  
Gastrointestinal Cancer ◽  
Aminolevulinic Acid ◽  
Primary Tumors ◽  
Cancer Cell Motility ◽  
Significant Difference

Abstract The aim of this study was the quantitative evaluation of gastrointestinal cancer cell motility and 5-aminolevulinic acid (5-ALA)-induced fluorescence in vitro using mathematical morphology and structural analysis methods. The results of our study showed that MKN28 cells derived from the lymph node have the highest motility compared with AGS or HCT116 cells derived from primary tumors. Regions of single cells were characterized as most moving, and “tightly packed” cell colonies as nearly immobile. We determined the reduction of cell motility in late passage compared to early passage. Application of 5-ALA caused fluorescence in all investigated cells, and the fluorescence was different with regard to the cell type and application time. We observed higher fluorescence in MKN28 cells. Comprehensive image analysis did not reveal any statistically significant difference in fluorescence intensity between “tightly packed” cell regions, where nearly no motility was registered and loosely distributed cells, where the highest cell motility was registered. In conclusions, our study revealed that MKN28 cells derived from the lymph node have higher motility and 5-ALA-induced fluorescence than AGS or HCT116 derived from primary tumors. Moreover, image analysis based on a large amount of processed data is an important tool to study these tumor cell properties.

Automatic Weave Image Tilt Correction Based on Interlacing Points Fullness

2011 ◽  
Vol 175-176 ◽  
pp. 928-932
Author(s):  
Chen Tao ◽  
Yan Chen
Keyword(s):  
Image Analysis ◽  
Mathematical Morphology ◽  
Image Preprocessing ◽  
Tilt Correction ◽  
Raster Scanning ◽  
Surface Distortions ◽  
The Relationship

As manual tilt correction has always been the bottleneck of automatic weave image analysis and processing, this paper proposed a means by which automatic weave image tilt correction can be achieved. First, mathematical morphology is carried out in weave image preprocessing for de-noise and enhancement. OTSU and raster scanning are followed to detect target rectangles in the image so as to determine the outlines of interlacing points on which the Interlacing Point Fullness (ISF) is established. Then the relationship between ISF and the correcting angle of weave image is discussed and the correcting coefficient built on ISF is used to counteract the disturbance of weave surface distortions in order to identify the correcting angle in a comprehensive way.

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