Tumor detection method of small animals on X-ray images

Yuji Karita, Toshiyuki Tanaka, Isao Kabaya, Mikiya Kano, Isamu Iwayoshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Tumor diagnosis by X-ray images is a standard way in small animals. But it is difficult to decide tumor region of X-ray images, and veterinarians have to diagnose many X-ray images. Therefore, there is increasing demand for the development of CAD (Computer Aided Diagnosis) system to support veterinarians. We use X-ray images of small animals such as dogs. In this paper, automatic detection of tumor region from X-ray images is studied. We use normalized correlation between original image and template image to emphasize tumor region. This template image is based on feature of tumor. We also use Quoit filter to detect tumor candidate regions. Then, we calculate two feature values, mean of intensity and roundness in these regions, and classify these two feature values into four patterns by K-means clustering to decide true tumor region. As a result, some tumor region can be detected.

Original languageEnglish
Title of host publicationSICE Annual Conference, SICE 2007
Pages698-702
Number of pages5
DOIs
Publication statusPublished - 2007 Dec 1
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
Duration: 2007 Sep 172007 Sep 20

Publication series

NameProceedings of the SICE Annual Conference

Other

OtherSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
CountryJapan
CityTakamatsu
Period07/9/1707/9/20

    Fingerprint

Keywords

  • Clustering
  • Correlation
  • Filter
  • Small animals
  • X-ray images

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Karita, Y., Tanaka, T., Kabaya, I., Kano, M., & Iwayoshi, I. (2007). Tumor detection method of small animals on X-ray images. In SICE Annual Conference, SICE 2007 (pp. 698-702). [4421072] (Proceedings of the SICE Annual Conference). https://doi.org/10.1109/SICE.2007.4421072