Special Issue "Machine Learning for Medical Imaging 2012"

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A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 August 2012)

Special Issue Editor

Guest Editor
Dr. Kenji Suzuki (Website)

Associate Professor of Electrical and Computer Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL 60616, USA
Phone: +1 312 567 5232
Interests: machine learning in medical imaging, computation intelligence in medical imaging, computer-aided detection and diagnosis, medical image processing and analysis

Special Issue Information

Dear Colleagues,

Medical imaging is an indispensable tool of patients’ healthcare in modern medicine. Machine learning plays an essential role in the medical imaging field, including medical image analysis, computer-aided diagnosis, organ/lesion segmentation, image fusion, image-guided therapy, image annotation and image retrieval, because objects such as lesions and anatomy in medical images cannot be represented accurately by simple equations; thus, tasks in medical imaging essentially require “learning from examples.” Because of its essential needs, machine learning for medical imaging is one of the most promising, rapidly growing fields. As medical imaging has been advancing with the introduction of new imaging modalities and methodologies such as cone-beam/multi-slice CT, positron-emission tomography (PET)-CT, tomosynthesis, diffusion-weighted magnetic resonance imaging (MRI), electrical impedance tomography and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Areas of interest in this special issue are all aspects of machine-learning research in the medical imaging field, including, but not limited to:

  • computer-aided detection/diagnosis (e.g., for lung cancer, breast cancer, colon cancer, liver cancer, acute disease, chronic disease, osteoporosis)
  • machine learning (e.g., with support vector machines, statistical methods, manifold-space-based methods, artificial neural networks, decision tree learning, Bayesian networks, sparse dictionary learning, genetic algorithms) applications to medical images with 2D, 3D and 4D data.
  • multi-modality fusion (e.g., PET-CT, projection X-ray-CT, X-ray-ultrasound)
  • medical image analysis (e.g., pattern recognition, classification, segmentation, registration) of lesions, lesion stage, organs, anatomy, status of disease and medical data
  • image reconstruction (e.g., expectation maximization (EM) algorithm, statistical methods) for medical images (e.g., CT, PET, MRI, X-ray)
  • biological image analysis (e.g., biological response monitoring, cell/marker tracking/detection)
  • image fusion of multiple modalities, multiple phases, multiple sequences and multiple angles
  • image retrieval (e.g., lesion similarity, context-based) and data mining
  • gene data analysis (e.g., genotype/phenotype classification/identification)
  • molecular/pathologic image analysis (e.g., PET, digital pathology)
  • dynamic, functional, physiologic, and anatomic imaging.

Dr. Kenji Suzuki
Guest Editor

Keywords

  • computer-aided diagnosis
  • support vector machines
  • artificial neural networks
  • manifold
  • decision tree learning
  • Bayesian networks
  • sparse dictionary learning
  • genetic algorithms
  • classificatio
  • pattern recognition
  • image reconstruction
  • registration
  • medical image analysis
  • statistical pattern recognition
  • segmentation
  • image fusion
  • image retrieval
  • biological imaging
  • multiple modalities
  • gene
  • X-ray
  • CT
  • MRI
  • PET
  • Ultrasound
  • digital pathology

Published Papers (2 papers)

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Research

Open AccessArticle Extraction and Segmentation of Sputum Cells for Lung Cancer Early Diagnosis
Algorithms 2013, 6(3), 512-531; doi:10.3390/a6030512
Received: 12 November 2012 / Revised: 14 August 2013 / Accepted: 15 August 2013 / Published: 21 August 2013
Cited by 7 | PDF Full-text (725 KB) | HTML Full-text | XML Full-text
Abstract
Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its symptoms can be found exclusively in advanced stages where the chances for patients to survive are very low, thus making the [...] Read more.
Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its symptoms can be found exclusively in advanced stages where the chances for patients to survive are very low, thus making the mortality rate the highest among all other types of cancer. The present work deals with the attempt to design computer-aided detection or diagnosis (CAD) systems for early detection of lung cancer based on the analysis of sputum color images. The aim is to reduce the false negative rate and to increase the true positive rate as much as possible. The early detection of lung cancer from sputum images is a challenging problem, due to both the structure of the cancer cells and the stained method which are employed in the formulation of the sputum cells. We present here a framework for the extraction and segmentation of sputum cells in sputum images using, respectively, a threshold classifier, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with other competitive techniques via a series of experimentation conducted with a data set of 100 images. The extraction and segmentation results will be used as a base for a CAD system for early detection of lung cancer which will improve the chances of survival for the patient. Full article
(This article belongs to the Special Issue Machine Learning for Medical Imaging 2012)
Open AccessArticle Mammographic Segmentation Using WaveCluster
Algorithms 2012, 5(3), 318-329; doi:10.3390/a5030318
Received: 2 June 2012 / Revised: 10 July 2012 / Accepted: 20 July 2012 / Published: 10 August 2012
Cited by 2 | PDF Full-text (582 KB) | HTML Full-text | XML Full-text
Abstract
Segmentation of clinically relevant regions from potentially noisy images represents a significant challenge in the field of mammography. We propose novel approaches based on the WaveCluster clustering algorithm for segmenting both the breast profile in the presence of significant acquisition noise and [...] Read more.
Segmentation of clinically relevant regions from potentially noisy images represents a significant challenge in the field of mammography. We propose novel approaches based on the WaveCluster clustering algorithm for segmenting both the breast profile in the presence of significant acquisition noise and segmenting regions of interest (ROIs) within the breast. Using prior manual segmentations performed by domain experts as ground truth data, we apply our method to 150 film mammograms with significant acquisition noise from the University of South Florida’s Digital Database for Screening Mammography. We then apply a similar segmentation procedure to detect the position and extent of suspicious regions of interest. Our approach was able to segment the breast profile from all 150 images, leaving minor residual noise adjacent to the breast in three. Performance on ROI extraction was also excellent, with 81% sensitivity and 0.96 false positives per image when measured against manually segmented ground truth ROIs. When not utilizing image morphology, our approach ran in linear time with the input size. These results highlight the potential of WaveCluster as a useful addition to the mammographic segmentation repertoire. Full article
(This article belongs to the Special Issue Machine Learning for Medical Imaging 2012)

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