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    Medical image compression and quality assessment
    (2013-01-01)
    Suapang, Piyamas
    ;
    Thongyoun, Methinee
    ;
    Chivapreecha, Sorawat
    In this research proposed to compressed medical images in single frame file format with two different techniques - JPEG and JPEG2000. The significant advantage of JPEG2000 over normal JPEG is that the error from JPEG2000 compression is smaller than the error from JPEG. Nevertheless, both methods share a similar mishap; when the compression ratio increases, they both generate more error than the processes on lower compression ratio do. What's more, the research proposed a neural network approach to image quality assessment. In particular, the neural network measures the quality of an image by predicting the mean opinion score (MOS) of human observers and using a set of key features extracted from the original and test images. Experimental results, using JPEG and JPEG2000 compressed images, show that the neural network outputs correlate highly with the MOS scores, and therefore, the neural network can easily serve as a correlate to subjective image quality assessment. The predicted MOS values have a linear correlation coefficient of0.9543, a Spearman ranked correlation of 0.9591.
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    A mammography database and viewer system
    (2012-12-01)
    Suapang, Piyamas
    ;
    Thongyoun, Methinee
    ;
    Boontawan, Rodjarin
    ;
    Chivaprecha, Sorawat
    A Mammography Database and Viewer System have collected and stored mammography images of Thai women, treated at the Radiology Department of Hospital in Rangsit, Pathumtani, Thailand, constructed a database using the digitized mammography images, and developed an image viewing system to display these digital mammograms. The images are stored with the high resolution Matrox Morphis (MOR/2VD/84*) frame grabber board. The digital mammography database is powered by MySQL and PHP. It also has a web-based search engine (with secure access) so that the information can be made accessible to all medical team members. The image viewing software, DICOM Viewer, is coded in Borland C++ Builder and is intended to help medical professionals view and retrieve large data sets in near real time. This have already digitized 200 cases (one case per patient, twenty to forty images per case). The system also have developed a MySQL-based image database, and a PHP-based web-search engine to retrieve cases that have masses (benign or malignant) or calcifications (benign or malignant). This database has been evaluated by medical professionals and the experimental results obtained so far are very promising (high image qualities, fast access time). The system are currently developing an image content-based retrieval function for the database system in order to provide improved search capability for the medical professionals. ©2012 IEEE.
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    Mammographic masses segmentation based on morphology
    (2012-12-01)
    Suapang, Piyamas
    ;
    Naruephai, Chadaporn
    ;
    Thongyoun, Methinee
    ;
    Chivaprecha, Sorawat
    Medical images segmentation is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, segmentation is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image segmentation. In this paper, basic morphological theory and operations are introduced at first, and then a novel morphological segmentation algorithm is proposed to detect the segment of mammographic masses with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and segmentation than the usually used template-based segmentation algorithms and general morphological segmentation algorithms. ©2012 IEEE.