Literature DB >> 18215905

Detection of spicules on mammogram based on skeleton analysis.

H Kobatake1, Y Yoshinaga.   

Abstract

Existence of spicules is one of important clues of malignant tumors. This paper presents a new image processing method for the detection of spicules on mammogram. Spicules can be recognized as line patterns radiating from the center of tumor. To detect such characteristic patterns, line skeletons and a modified Hough transform are proposed. Line skeleton processing is effective in enhancing spinal axes of spicules and in reducing the other skeletons. The modified Hough transform is applied to line skeletons and radiating line structures are obtained. Experiments were made to test the performance of the proposed method. The system was designed using 19 training images, for which one normal case was recognized to be star-shaped. The other case were recognized correctly. Another experiments using 34 test images were also performed. The correct classification rate was 74%. These results shows the effectiveness of the proposed method.

Entities:  

Year:  1996        PMID: 18215905     DOI: 10.1109/42.500062

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  9 in total

1.  Boundary modelling and shape analysis methods for classification of mammographic masses.

Authors:  R M Rangayyan; N R Mudigonda; J E Desautels
Journal:  Med Biol Eng Comput       Date:  2000-09       Impact factor: 2.602

2.  Dual system approach to computer-aided detection of breast masses on mammograms.

Authors:  Jun Wei; Heang-Ping Chan; Berkman Sahiner; Lubomir M Hadjiiski; Mark A Helvie; Marilyn A Roubidoux; Chuan Zhou; Jun Ge
Journal:  Med Phys       Date:  2006-11       Impact factor: 4.071

3.  A combined approach for the enhancement and segmentation of mammograms using modified fuzzy C-means method in wavelet domain.

Authors:  Subodh Srivastava; Neeraj Sharma; S K Singh; R Srivastava
Journal:  J Med Phys       Date:  2014-07

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

Authors:  Yoshitaka Kimori
Journal:  J Clin Bioinforma       Date:  2011-12-16

5.  Mass segmentation using a combined method for cancer detection.

Authors:  Jun Liu; Jianxun Chen; Xiaoming Liu; Lei Chun; Jinshan Tang; Youping Deng
Journal:  BMC Syst Biol       Date:  2011-12-23

6.  Morphological image processing for quantitative shape analysis of biomedical structures: effective contrast enhancement.

Authors:  Yoshitaka Kimori
Journal:  J Synchrotron Radiat       Date:  2013-09-25       Impact factor: 2.616

7.  Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy.

Authors:  Joonmyeong Choi; Keewon Shin; Jinhoon Jung; Hyun-Jin Bae; Do Hoon Kim; Jeong-Sik Byeon; Namku Kim
Journal:  Clin Endosc       Date:  2020-03-30

8.  High Precision Mammography Lesion Identification From Imprecise Medical Annotations.

Authors:  Ulzee An; Ankit Bhardwaj; Khader Shameer; Lakshminarayanan Subramanian
Journal:  Front Big Data       Date:  2021-12-03

9.  Introducing kernel based morphology as an enhancement method for mass classification on mammography.

Authors:  Azardokht Amirzadi; Reza Azmi
Journal:  J Med Signals Sens       Date:  2013-04
  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.