000			03220nam a2200265 a 4500
001			vtls000075323
003			KUKTEM
005			20251114204552.0
008			131011t2013 my da f m 000 0 eng d
020			_aTHE0002948(Local)
039		9	_a201905140835 _bzul _c201710031603 _daishah _c201311201624 _dnabilah _y201310111456 _znabilah
040			_aUMP
090			_aTA1637 .M34 2013 rs Thesis
100	1		_aAhmed, Muhammad Mahmood
245	1	0	_aContrast optimization by region adaptation (COBRA) for grey scale images / _cMuhammad Mahmood Ahmed
260			_aKuantan, Pahang : _bUMP, _c2013
300			_axv, 124 p. : _bill. ; _c30 cm. + _e1 CD-ROM
502			_aThesis (Master of Science (Computer Science)) -- Universiti Malaysia Pahang – 2013
504			_aBibliography : p. 111-116
520	3		_aMedical images form a part of real world images which come with a wide variety of contrast and brightness. The acquired images almost invariably require contrast enhancement. Some of the underlying contrast enhancement methods do not produce predictable results. Contemporary contrast enhancement frequently relies on histogram equalization (HE). In practice, HE produces unexpected results. Such, inconsistent results make reliability of HE questionable. As a result HE is unacceptable in sensitive areas like medical field. The situation leads to a detailed analysis of HE, which brings out that foundation of HE is based on density not contrast. As this foundation is unrelated to contrast, resulting contrast changes are unpredictable. As a solution, a novel method based on factors directly related to contrast is proposed. This method separates the image into dark and bright regions. Based on this concept the proposed method named Contrast Optimization by Region Adaptation (COBRA) is developed by optimizing the contrast ratio of separated regions. To achieve this optimization, the whole image is shrunk to a lower scale which provides necessary space for readjustment of contrast ratio. Then the constituents grey levels are raised exponentially to revert back to original scale. This exponential reversion, adjusts the contrast ratio of separated regions to optimum. This contrast optimization fulfills deficiency in real world images. Due to contrast based foundation of the proposed method, the resultant enhancement in similar type of images is consistent. These predictable results, yield high reliability which makes the proposed method trustworthy for critical areas like medical field. Additionally the results reveal that the histogram of the enhanced image represents similarity with the original image. This similarity is measured using DICE and Jaccard methods. Based on the similarity figures, comparative analysis was carried out between the proposed method and HE. The analysis verified that the proposed method achieves excellent results on brain MRIs, additionally it performs well on general medical and common bench mark images.
650		0	_aBrain _xMagnetic resonance imaging
650		0	_aImage processing
856	4	0	_uhttp://ecollib.ump.edu.my/24801/ _zAccess in library only
999			_aVIRTUA40 _c4275 _d4281
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*8560*9992