Computational and Mathematical Methods in Medicine

Computer-Aided Detection and Diagnosis in Medical Imaging


Publishing date
07 Jun 2013
Status
Published
Submission deadline
18 Jan 2013

Lead Editor

1National Taiwan University, Taipei, Taiwan

2Taipei Veterans General Hospital and National Yang Ming University, Taipei, Taiwan

3Tokyo Metropolitan University, Tokyo, Japan

4Kyungpook National University, Daegu, South Korea


Computer-Aided Detection and Diagnosis in Medical Imaging

Description

Medical images nowadays play an essential role in detection and diagnosis of numerous diseases. Ranging from anatomical information, functional activities, to the molecular and cellular expressions, medical imaging provides direct visualization means to see through the human bodies and observe the minute anatomical changes and biological processes characterized by different physical and biological parameters. Informative as they are, medical imaging usually requires experienced medical doctors to best interpret the information revealed in the images. However, because of various subjective factors as well as limited analysis time and tools, it is quite common that different medical doctors may come up with diverse interpretations, leading to different diagnoses. Moreover, for the same set of medical imaging, a medical doctor may make different diagnosis results at different time.

To attain a more reliable and accurate diagnosis, recently, varieties of computer-aided detection (CAD) and diagnosis (CADx) approaches have been developed to assist interpretation of the medical images. At least four types of efforts may be identified among these CAD and CADx approaches. The first type is to assist in visual detection and qualitative analysis of the objects of interest in the medical images by either enhancing the salient features of the objects or suppressing the background noises. The second type is to assist in extraction of the objects of interest for further quantitative analyses by such techniques as boundary delineation, tree-structure reconstruction, and fiber tracking. The third type is to automatically detect and classify the objects of interest by integrating the data mining, medical image analysis, and signal processing technologies. The fourth type is to estimate the anatomical and functional tissue properties not explicitly revealed in the medical images based on mathematical modeling, for example, physiology, biomechanics, heat transfer, and so forth.

This special issue aims to present the state-of-the-art CAD and CADx algorithms for medical images. While all four types of works are welcome, those new CAD and CADx algorithms with clinical or biological validation are particularly encouraged. Potential topics include, but are not limited to:

  • Enhancement and visualization methods for improving visual detection and diagnosis
  • Segmentation algorithms for 3D lesions, organs, skeletons, vessels, airways, and so on
  • Fiber structure extraction algorithms
  • Cross-sectional, longitudinal, and multimodality image registration algorithms
  • Lesion and abnormality detection algorithms in volume images
  • High-performance CAD and CADx algorithms for clinical applications
  • Anatomical and functional parameter estimation based on mathematical modeling

Before submission authors should carefully read over the journal's Author Guidelines, which are located at http://www.hindawi.com/journals/cmmm/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/submit/journals/cmmm/cad/ according to the following timetable:


Articles

  • Special Issue
  • - Volume 2013
  • - Article ID 790608
  • - Editorial

Computer-Aided Detection and Diagnosis in Medical Imaging

Chung-Ming Chen | Yi-Hong Chou | ... | Younghae Do
  • Special Issue
  • - Volume 2013
  • - Article ID 630902
  • - Research Article

Statistical Texture Modeling for Medical Volume Using Linear Tensor Coding

Junping Deng | Xu Qiao | Yen-Wei Chen
  • Special Issue
  • - Volume 2013
  • - Article ID 148363
  • - Research Article

Classification of Pulmonary Nodules by Using Hybrid Features

Ahmet Tartar | Niyazi Kilic | Aydin Akan
  • Special Issue
  • - Volume 2013
  • - Article ID 593175
  • - Research Article

Segmentation of the Striatum from MR Brain Images to Calculate the -TRODAT-1 Binding Ratio in SPECT Images

Ching-Fen Jiang | Chiung-Chih Chang | ... | Chia-Hsiang Wu
  • Special Issue
  • - Volume 2013
  • - Article ID 213901
  • - Research Article

Customized First and Second Order Statistics Based Operators to Support Advanced Texture Analysis of MRI Images

Danilo Avola | Luigi Cinque | Giuseppe Placidi
  • Special Issue
  • - Volume 2013
  • - Article ID 619658
  • - Research Article

Classification of Cerebral Lymphomas and Glioblastomas Featuring Luminance Distribution Analysis

Toshihiko Yamasaki | Tsuhan Chen | ... | Ryuji Murakami
  • Special Issue
  • - Volume 2013
  • - Article ID 914124
  • - Research Article

Computer Aided Quantification of Pathological Features for Flexor Tendon Pulleys on Microscopic Images

Yung-Chun Liu | Hsin-Chen Chen | ... | Yung-Nien Sun
  • Special Issue
  • - Volume 2013
  • - Article ID 196259
  • - Research Article

Particle System Based Adaptive Sampling on Spherical Parameter Space to Improve the MDL Method for Construction of Statistical Shape Models

Rui Xu | Xiangrong Zhou | ... | Hiroshi Fujita
  • Special Issue
  • - Volume 2013
  • - Article ID 832509
  • - Review Article

Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making

Turgay Ayer | Qiushi Chen | Elizabeth S. Burnside
  • Special Issue
  • - Volume 2013
  • - Article ID 482941
  • - Research Article

GND-PCA-Based Statistical Modeling of Diaphragm Motion Extracted from 4D MRI

Windra Swastika | Yoshitada Masuda | ... | Hideaki Haneishi
Computational and Mathematical Methods in Medicine
 Journal metrics
Acceptance rate38%
Submission to final decision61 days
Acceptance to publication39 days
CiteScore1.840
Impact Factor1.563
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