Abstract
Emerging bioimaging technologies enable us to capture various dynamic cellular activities [Formula: see text]. As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone remodeling, osteoporosis, and so on. To reduce manual inputs to segment BMC, we classified the texture pattern using wavelet transformation and support vector machine. We also integrated the result of texture pattern classification into the graph-cuts-based image segmentation method because texture analysis does not consider spatial continuity. Our method is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The proposed method with graph-cuts and texture pattern classification performs well without manual inputs by a user.
Highlights
Advance in bioimaging technologies such as microscopic imaging techniques has made it possible to capture various dynamic cellular activities in vivo
To reduce manual inputs and to achieve automatic segmentation, we propose a method for detecting bone marrow cavity (BMC) regions by texture analysis with wavelet transformation (WT) and classifying by using support vector machine (SVM)
1; BMC; 0; otherwise; where Iv is intensity of pixel v in the green channel that is dened as 0 Iv 1, gc is the result of classication by the SVM, is a weight parameter for the result of texture pattern classication, and is a constant value to choose suitable to the input images
Summary
Advance in bioimaging technologies such as microscopic imaging techniques has made it possible to capture various dynamic cellular activities in vivo. These technologies are expected to contribute to the discovery of new drugs and will clarify. Multiphoton excitation microscopy is one of the new imaging technologies that can observe deeply the cellular activities in living tissues in vivo. It was previously di±cult to observe bone marrow in vivo because it resides inside of hard bones which mainly consist of calcium. Multiphoton excitation microscopy enabled observation images inside bone marrow in vivo, such as blood °ow and cellular activities. Since the output of texture analysis does not consider spatial continuity, we apply graph-cuts for image segmentation with the results of wavelet analysis
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of bioinformatics and computational biology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
