Computer aided system for segmentation and visualization of microcalcifications in digital mammograms.

Abstract

Abstract:Two methods for segmentation and visualization of microcalcifications in digital or digitized mammograms are described. First method is based on modern mathematical morphology, while the second one uses the multifractal approach. In the first method, by using an appropriate combination of some morphological operations, high local contrast enhancement, followed by significant suppression of background tissue, irrespective of its radiology density, is obtained. By iterative procedure, this method highly emphasizes only small bright details, possible microcalcifications. In a multifractal approach, from initial mammogram image, a corresponding multifractal ''images'' are created, from which a radiologist has a freedom to change the level of segmentation. An appropriate user friendly computer aided visualization (CAV) system with embedded two methods is realized. The interactive approach enables the physician to control the level and the quality of segmentation. Suggested methods were tested through mammograms from MIAS database as a gold standard, and from clinical praxis, using digitized films and digital images from full field digital mammograph.

Mamografija - sta bi lekar opste medicine trebalo da zna?

“…Komparativna analiza analogne i digitalne mamografije u skriningu karcinoma doj ke koju su objavili 2005. godine Pisano i sar. dokazala je prednost digitalne mamografije u detekciji subkliničkih karcinoma kod žena životne dobi 50 godina i mlađih, premenopauznih i perimenopauznih žena i žena sa glandularnom (denznom) bazičnom građom dojki [3]. Dalji razvoj digitalne tehnologije uvodi automatizovanu detekciju patoloških promena na mamografiji namenskim programima za naknadnu obradu slike (computer aided detection, CAD) [4].…”

Application of multifractal analysis on microscopic images in the classification of metastatic bone disease

“…2.1 Applying multifractal analysis in research of cancer Many researches have shown existing correlation between fractal and multifractal parameters and the tumours, or pathology forms (Baish et al 1998;Budimlija 2001;Buldyrev et al 1994;Bulmore et al 1994;Bunde et al 1990Bunde et al , 1991Byng et al 1996;Fenoglio-Preiser et al 1989;Claridge et al 1992;Coffey 1998;Cross 1994Cross , 1997Reljin et al 2000Reljin et al , 2002Reljin et al , 2008Reljin et al , 2009Stojic et al 2006;Ballerini et al 2003;Roy et al 2011;Mukundan and Hemsley 2010). It has been shown that tissue images contain several fractal structures with varying dimension at varying strengths (Reljin et al 2000(Reljin et al , 2002Mukundan and Hemsley 2010).…”

“…A smaller number of studies researches possibilities (for Ref. see Mukundan and Hemsley 2010) of applying multifractal parameters in the classification of medical images and thus in the medical diagnostics (Reljin et al 2002(Reljin et al , 2009(Reljin et al , 2011Bartnykas 2010). The results of these studies point to a field of great potential for further study in order to increase the reliability of cancer diagnosis and its acceleration and facilitation.…”

Mathematical morphology-based approach to the enhancement of morphological features in medical images

“…Furthermore, size information in the image can also be obtained by using the granulometry which is an approach to measure a size distribution of objects [18]. Several morphological contrast enhancement methods have been applied to medical images [19-22]. These methods enable detection of lesions of various sizes and shapes, including complex shapes.…”