MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03220nam a2200265 a 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
vtls000075323 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251114204552.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
131011t2013 my da f m 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0002948(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
|---|
| Level of rules in bibliographic description |
201905140835 |
| Level of effort used to assign nonsubject heading access points |
zul |
| Level of effort used to assign subject headings |
201710031603 |
| Level of effort used to assign classification |
aishah |
| Level of effort used to assign subject headings |
201311201624 |
| Level of effort used to assign classification |
nabilah |
| -- |
201310111456 |
| -- |
nabilah |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
TA1637 .M34 2013 rs Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ahmed, Muhammad Mahmood |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Contrast optimization by region adaptation (COBRA) for grey scale images / |
| Statement of responsibility, etc. |
Muhammad Mahmood Ahmed |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2013 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 124 p. : |
| Other physical details |
ill. ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Master of Science (Computer Science)) -- Universiti Malaysia Pahang – 2013 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Bibliography : p. 111-116 |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Medical 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 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Brain |
| General subdivision |
Magnetic resonance imaging |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Image processing |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="http://ecollib.ump.edu.my/24801/">http://ecollib.ump.edu.my/24801/</a> |
| Public note |
Access in library only |