𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Adaptation of multifractal analysis to segmentation of microcalcifications in digital mammograms

✍ Scribed by Tomislav Stojić; Irini Reljin; Branimir Reljin


Book ID
104078094
Publisher
Elsevier Science
Year
2006
Tongue
English
Weight
598 KB
Volume
367
Category
Article
ISSN
0378-4371

No coin nor oath required. For personal study only.

✦ Synopsis


A method for detecting microcalcifications in digital mammograms is proposed. After recognizing basic features of microcalcifications we introduced several modifications in multifractal analysis, obtaining an efficient method adapted to enhance only small light parts not belonging to surrounding tissue, possibly microcalcifications. Started with a mammogram image, a method creates corresponding multifractal image from which a radiologist has the freedom to change the level of segmentation in an interactive manner and to find suspicious regions, which may contain microcalcifications. Additional postprocessing, based on mathematical morphology, refines the procedure by selecting and outlining regions that contain clusters with microcalcifications. The proposed method was tested through referent mammograms from MiniMIAS database, which is available at public domain. The proposed method successfully extracted microcalcifications in all (clinically approved) cases belonging to this database.


📜 SIMILAR VOLUMES


Interactive Selective and Adaptive Clust
✍ Leonardo Estevez; Nasser Kehtarnavaz; Richard Wendt III 📂 Article 📅 1996 🏛 Elsevier Science 🌐 English ⚖ 794 KB

This paper presents a clustering algorithm, called interactive selective and adaptive clustering (Isaac), to assist radiologists in looking for small clusters of microcalcifications in mammograms. Isaac is developed to identify suspicious microcalcification regions which are missed by other classifi

An improved method of region grouping fo
✍ Wei Qian; Fei Mao; Xuejun Sun; Yan Zhang; Dansheg Song; Robert A. Clarke 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 246 KB

A very important issue, namely region grouping, in computer-assisted diagnostic detection of microcalcification clusters (MCC) in digital mammograms is addressed in this work. In the diagnosis of breast cancer, MCC, instead of single and isolated microcalcifications, are considered clinically signif