scholarly journals A fast and simple method for the visual enhancement of microcalcifications in digital mammograms based on mathematical morphology

2014 ◽  
Vol 29 (2) ◽  
pp. 108-115
Author(s):  
Tomislav Stojic
Keyword(s):  
Image Analysis ◽  
Real Time ◽  
Mathematical Morphology ◽  
Simple Method ◽  
Processing Task ◽  
Structuring Element ◽  
Visual Enhancement ◽  
Visual Aid ◽  
Analysis Society ◽  
Shape And Size

A fast and simple method for the visual enhancement of small bright details in digital mam- mograms based on mathematical morphology is proposed. By a proper choice of the shape and size of the structuring element, an algorithm for a particular processing task - in this case, for the visual enhancement of microcalcifications in digital mammograms - was designed. The efficiency of the proposed algorithm was tested on publicly available mammograms from the mammographic image analysis society database. In all tested cases (23 mammograms), the proposed method successfully segmented and enhanced the existing microcalcifications, in- dependently verified by medical experts. The proposed procedure may be used both as a visual aid in clinical mammogram analysis or as a preprocessing step for further processing, such as segmentation, classification and detection of microcalcifications. Moreover, the algorithm is very fast and robust, thus applicable to real-time mammogram processing.

scholarly journals Visual enhancement of microcalcifications and masses in digital mammograms using modified multifractal analysis

2015 ◽  
Vol 30 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Tomislav Stojic
Keyword(s):  
Image Analysis ◽  
Mathematical Morphology ◽  
Multifractal Analysis ◽  
Breast Tissue ◽  
Surrounding Tissue ◽  
Local Contrast ◽  
Post Processing ◽  
Common Features ◽  
Visual Enhancement ◽  
Mammogram Image

Microcalcifications and masses, as breast tissue anomalies (deviations from observed background regularity), may be viewed as statistically rare occurrences in a mammogram image. After recognizing their principal common features - bright image parts not belonging to the surrounding tissue, with significant local contrast just around the edges - several modifications to multifractal image analysis have been introduced. Starting from a mammogram image, the proposed method creates corresponding multifractal images. Additional post-processing, based on mathematical morphology, refines the procedure by selecting and outlining only regions with possible microcalcifications and masses. The proposed method was tested through referent mammograms from the MiniMIAS database. In all cases involving the said database, the method has successfully enhanced declared anomalies: microcalcifications and masses. The results obtained have shown that the described procedure may provide visual assistance to radiologists in clinical mammogram examinations or be used as a preprocessing step for further mammogram processing, such as segmentation, classification, and automatic detection of suspected bright breast tissue lesions.

scholarly journals Consolidated morphology

2006 ◽  
Author(s):  
Gaetan Lehmann ◽  
Richard Beare
Keyword(s):  
Image Analysis ◽  
Mathematical Morphology ◽  
Computation Time ◽  
3D Images ◽  
Structuring Element

Grayscale dilation and erosion are basic transformations of mathematical morphology. Used together or with other transformations, they are very useful tools for image analysis. However, they can be very time consuming, especially with 3D images, and with large structuring elements. Several algorithms have been created to decrease the computation time, some of them with some limitations of shape of structuring element. We have implemented several algorithms, studied their performance in different conditions, and shown that all of them are more efficients than the others in certain conditions. We finally introduce a new structuring element class and a some meta filter designed to select the best algorithm depending on the image and the structuring element, and to smoothely integrate the different algorithms available in the toolkit.

Real-time scanning and image analysis

1988 ◽  
Vol 109 (1) ◽  
pp. 131-137 ◽  
Author(s):  
Jens Oluf Pedersen ◽  
Lars Hassing ◽  
Grunnet Niels ◽  
Casper Jersild
Keyword(s):  
Image Analysis ◽  
Real Time ◽  
Time Scanning

Measuring Coral Skeletal Architecture Using Image Analysis v1

2021 ◽  
Author(s):  
Rowan Mclachlan ◽  
Ashruti Patel ◽  
Andrea G Grottoli
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Image Analysis ◽  
Scleractinian Coral ◽  
Coral Species ◽  
Energy Materials ◽  
Simple Method ◽  
Coral Skeletons ◽  
Skeletal Structures ◽  
Scanning Electron

Coral morphology is influenced by genetics, the environment, or the interaction of both, and thus is highly variable. This protocol outlines a non-destructive and relatively simple method for measuring Scleractinian coral sub-corallite skeletal structures (such as the septa length, theca thickness, and corallite diameter, etc.) using digital images produced as a result of digital microscopy or from scanning electron microscopy. This method uses X and Y coordinates of points placed onto photomicrographs to automatically calculate the length and/or diameter of a variety of sub-corallite skeletal structures in the Scleractinian coral Porites lobata. However, this protocol can be easily adapted for other coral species - the only difference may be the specific skeletal structures that are measured (for example, not all coral species have a pronounced columella or pali, or even circular corallites). This protocol is adapted from the methods described in Forsman et al. (2015) & Tisthammer et al. (2018). There are 4 steps to this protocol: 1) Removal of Organic Tissue from Coral Skeletons 2) Imaging of Coral Skeletons 3) Photomicrograph Image Analysis 4) Calculation of Corallite Microstructure Size This protocol was written by Dr. Rowan McLachlan and was reviewed by Ashruti Patel and Dr. Andréa Grottoli. Acknowledgments Leica DMS 1000 and Scanning Electron Microscopy photomicrographs used in this protocol were acquired at the Subsurface Energy Materials Characterization and Analysis Laboratory (SEMCAL), School of Earth Sciences at The Ohio State University, Ohio, USA. I would like to thank Dr. Julie Sheets, Dr. Sue Welch, and Dr. David Cole for training me on the use of these instruments.

SMART SENSOR: AN ON-BOARD IMAGE PROCESSING SYSTEM FOR REAL-TIME REMOTE SENSING

2002 ◽  
Vol 02 (03) ◽  
pp. 481-499
Author(s):  
JANE YOU ◽  
DAVID ZHANG
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Compression ◽  
Real Time ◽  
Processing System ◽  
Smart Sensor ◽  
Pipeline Architecture ◽  
New Approach ◽  
Hardware Processing ◽  
Sensor System Design

This paper presents a new approach to smart sensor system design for real-time remote sensing. A combination of techniques for image analysis and image compression is investigated. The proposed algorithms include: (1) a fractional discrimination function for image analysis, (2) a comparison of effective algorithms for image compression, (3) a pipeline architecture for parallel image classification and compression on-board satellites, and (4) a task control strategy for mapping image computing models to hardware processing elements. The efficiency and accuracy of the proposed techniques are demonstrated throughout system simulation.

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