From medical imaging to medical informatics

Müller, Henning; Gao, Xiaohong; Luo, Shuqian

doi:10.1016/j.cmpb.2008.07.004

Cited by 7 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The computerized medical activities in hospitals using picture archiving and communication systems (PACS) have generated a critical need for efficient and powerful computerized systems [1], including those that would help the physicians in the diagnosis. Automated Computer-Aided Diagnosis (CAD) systems are playing an important role in modern medical practices [2].…”

Section: Introductionmentioning

confidence: 99%

Using Sub-dictionaries for Image Representation Based on the Bag-of-Visual-Words Approach

Pedrosa

Traina

2014

2014 IEEE 27th International Symposium on Computer-Based Medical Systems

View full text Add to dashboard Cite

Bag-of-Visual-Words (BoVW) is a well known approach to represent images for visual recognition and retrieval tasks. This approach represents an image as a histogram of visual words and the dissimilarity between two images is measured by comparing those histograms. When performing comparisons involving a specific type of images, some visual words can be more informative and discriminative than others. To take advantage of this fact, assigning appropriate weights can improve the performance of image retrieval. In this paper, we developed a novel modeling approach based on sub-dictionaries. We extracted a sub-dictionary as a subset of visual words that best represents a specific image class. To measure the dissimilarity distance between images, we take into account the distance of the histogram obtained using the visual dictionary and the distances of the sub-histograms obtained by each subdictionary. The proposed approach was evaluated by classifying a standard biomedical image dataset into categories defined by image modality and body part and also natural image scenes. The experimental results demonstrate the gain obtained of the proposed weighting approach when compared to the traditional weighting approach based on TF-IDF (Term Frequency-Inverse Document Frequency). Our proposed approach has shown promising results to boost the classification accuracy as well as the retrieval precision. Moreover, it does that without increasing the feature vector dimensionality.

show abstract

Section: Introductionmentioning

confidence: 99%

Using Sub-dictionaries for Image Representation Based on the Bag-of-Visual-Words Approach

Pedrosa

Traina

2014

2014 IEEE 27th International Symposium on Computer-Based Medical Systems

View full text Add to dashboard Cite

show abstract

“…As a field of scientific investigation, medical imaging constitutes a sub-discipline of biomedical engineering, medical physics or medicine depending on the context: Research and development in the area of instrumentation, image acquisition, modeling and quantification are usually the preserve of biomedical engineering, medical physics and computer science; Research into the application and interpretation of medical images is usually the preserve of radiology and the medical sub-discipline relevant to medical condition or area of medical science under investigation [4][5][6]. Many of the techniques developed for medical imaging also have scientific and industrial applications.…”

Section: Introductionmentioning

confidence: 99%

A fast reading and processing method of batch human CT image data

Zheng

Gui

2009

2009 ISECS International Colloquium on Computing, Communication, Control, and Management

View full text Add to dashboard Cite

With the further application of medical images data in medical field, in order to enrich and enhance the useful information in different medical images, image data fast reading and processing is increasingly becoming a promising research. In this work, we proposed a fast reading and processing method for batch human CT Image data. We can use some conventional image processing algorithm for fast processing of CT image. Experiment results shows proposed methods is robust, easy to implement and uses simple conventional image processing algorithm implementation. This novel medical images data batch processing methods can assist doctors in making more accurate diagnoses and treating patients in a more convenient way.

show abstract

“…Integration of newly developed methods into clinical routine is one of the major challenges in medical imaging [1,2]. In order to achieve this integration, new methods must be evaluated in realistic clinical settings by medical end-users who expect a certain level of robustness and usability [3,4].…”

Section: Introductionmentioning

confidence: 99%