Performance of Compound Enhancement Algorithms on Dental Radiograph Images
The purpose of this research is to compare the original
intra-oral digital dental radiograph images with images that are
enhanced using a combination of image processing algorithms. Intraoral
digital dental radiograph images are often noisy, blur edges and
low in contrast. A combination of sharpening and enhancement
method are used to overcome these problems. Three types of
proposed compound algorithms used are Sharp Adaptive Histogram
Equalization (SAHE), Sharp Median Adaptive Histogram
Equalization (SMAHE) and Sharp Contrast adaptive histogram
equalization (SCLAHE). This paper presents an initial study of the
perception of six dentists on the details of abnormal pathologies and
improvement of image quality in ten intra-oral radiographs. The
research focus on the detection of only three types of pathology
which is periapical radiolucency, widen periodontal ligament space
and loss of lamina dura. The overall result shows that SCLAHE-s
slightly improve the appearance of dental abnormalities- over the
original image and also outperform the other two proposed
compound algorithms.
[1] B.Kullendorff and M.Nilsson, "Diagnostic accuracy of direct digital
dental radiograph for the detection of periapical bone lesions", Oral.
Surg.Oral. Med. Oral. Pathol. Oral Radiol Endod, vol:82,pp:585-589,
1996
[2] M.Mehdizadeh and S.Dolatyar, "Study of Effect of Adaptive Histogram
Equalization on Image Quality in Digital Preapical Image in Pre Apex
Area", research Journal of Biological Science",pp: 922 - 924, vol: 4,
issue: 8, 2009
[3] W.E.G.W.Alves,E.Ono,J.L.O.Tanaka,E.M.Filho,L.C.Moraes,M.E.L.Mor
aes and J.C.M.Castilho, "Influence of image filters on the reproducibility
of measurements of aveolar bone loss", Journal of Applied Oral Science,
Vol. 4(6), pp: 415 - 420, 2006.
[4] N.M.Noor, N.E.A.Khalid M.H.Ali and A.D.A.Numpang,"Enhancement
of Soft Tissue Lateral Neck Radiograph with Fish Bone Impaction Using
Adaptive Histogram Equalization(AHE),The 2nd International
Conference on Computer Research and Development,2010.
[5] N.R.S.Praveen, M.Phil, and M.Sathik, "Enhancement of Bone Fracture
Images by Equalization Methods", International Conference on
Computer Technology and Development",pp: 391-394, 2009.
[6] N.E.A.Khalid, N. E., Manaf, M., Aziz, M. E., & Ali, M. H. (2007, Nov.
25-28). CR images of metacarpel cortical edge detection-bone profile
histogram approximation method. Intelligent and Advanced Systems,.
ICIAS ,pp: 702-708, 2007
[7] T.Kitasaka, K.Mori, J.Hasegawa and J.Toriwaki,"A Method for
Extraction of Bronchus Regions from 3D Chest X-ray CT Images by
Analyzing Structural Features of the Bronchus",FORMA, Vol:17,No. 4,
pp: 321- 338, 2002.
[8] H.Yoon,Y.Han, and H.Hahn,"Image Contrast Enhancement based on
Sub-histogram Equalization Techniques without Over-equalization
Noise", International Journal of Computer Science and Engineering
3:2,2009.
[9] P.Rahmati,G.Hamarneh, D.Nussbaum and A.Adler, " A New
Preprocessing Filter for Digital Mammograms, Lecture Notesin
Computer Science,vol: 6134, pp: 585-592, 2010.
[10] P.Jagatheeswari, S.S.Kumar and M.Rajaram, " A Novel Approach for
Contrast Enhancement Based on Histogram Equalization Followed By
Median Filter", ARPN Journal of Engineering and Applied Sciences,
Vol. 4, No.7, 2009
[11] W.Zhiming and T.Jianhua, "A fast implementation of Adaptive
Histogram Equalization", 8th International Conference on Signal
Processing (ICSP), 2006
[12] O.E.Langland, R.P.Langlais and J.W.Preece, "Principles of Dental
Imaging", Lippincott Williams & Wilkins, 2002.
[13] O.Molven, A.Halse, I.Fristad and M.Jankowski, " Periapical changes
following root-canal treatment observed 20-27 years postoperatively",
International Journal of Endodontic Research, vol: 35(9), pp: 784-90,
2002
[14] ImageJ , Image Processing and Analysis in Java official website
http://rsbweb.nih.gov/ij/.
[15] Allen, B. Wilkinson M. Parallel Programming, Techniques and
Applications Using Networked Workstations and Parallel Computers,
Pearson,2005.
[16] J.Poulist and M.Aubin, "Contrast Limited Adaptive Histogram
Equalization(CLAHE),http://radonc.ucsf.edu/research_group/jpouliot/Tu
torial/HU/Lesson7.htm, accessed on 4th June 2010.
[17] E.Pisano et al,"Contrast Limites Adaptive Histogram Equalization Image
Processing to Improve the Detection of Simulated Spiculations in Dense
Mammograms", Journal of Digital Imaging",Vol 11, pp: 193-200, 1998
[1] B.Kullendorff and M.Nilsson, "Diagnostic accuracy of direct digital
dental radiograph for the detection of periapical bone lesions", Oral.
Surg.Oral. Med. Oral. Pathol. Oral Radiol Endod, vol:82,pp:585-589,
1996
[2] M.Mehdizadeh and S.Dolatyar, "Study of Effect of Adaptive Histogram
Equalization on Image Quality in Digital Preapical Image in Pre Apex
Area", research Journal of Biological Science",pp: 922 - 924, vol: 4,
issue: 8, 2009
[3] W.E.G.W.Alves,E.Ono,J.L.O.Tanaka,E.M.Filho,L.C.Moraes,M.E.L.Mor
aes and J.C.M.Castilho, "Influence of image filters on the reproducibility
of measurements of aveolar bone loss", Journal of Applied Oral Science,
Vol. 4(6), pp: 415 - 420, 2006.
[4] N.M.Noor, N.E.A.Khalid M.H.Ali and A.D.A.Numpang,"Enhancement
of Soft Tissue Lateral Neck Radiograph with Fish Bone Impaction Using
Adaptive Histogram Equalization(AHE),The 2nd International
Conference on Computer Research and Development,2010.
[5] N.R.S.Praveen, M.Phil, and M.Sathik, "Enhancement of Bone Fracture
Images by Equalization Methods", International Conference on
Computer Technology and Development",pp: 391-394, 2009.
[6] N.E.A.Khalid, N. E., Manaf, M., Aziz, M. E., & Ali, M. H. (2007, Nov.
25-28). CR images of metacarpel cortical edge detection-bone profile
histogram approximation method. Intelligent and Advanced Systems,.
ICIAS ,pp: 702-708, 2007
[7] T.Kitasaka, K.Mori, J.Hasegawa and J.Toriwaki,"A Method for
Extraction of Bronchus Regions from 3D Chest X-ray CT Images by
Analyzing Structural Features of the Bronchus",FORMA, Vol:17,No. 4,
pp: 321- 338, 2002.
[8] H.Yoon,Y.Han, and H.Hahn,"Image Contrast Enhancement based on
Sub-histogram Equalization Techniques without Over-equalization
Noise", International Journal of Computer Science and Engineering
3:2,2009.
[9] P.Rahmati,G.Hamarneh, D.Nussbaum and A.Adler, " A New
Preprocessing Filter for Digital Mammograms, Lecture Notesin
Computer Science,vol: 6134, pp: 585-592, 2010.
[10] P.Jagatheeswari, S.S.Kumar and M.Rajaram, " A Novel Approach for
Contrast Enhancement Based on Histogram Equalization Followed By
Median Filter", ARPN Journal of Engineering and Applied Sciences,
Vol. 4, No.7, 2009
[11] W.Zhiming and T.Jianhua, "A fast implementation of Adaptive
Histogram Equalization", 8th International Conference on Signal
Processing (ICSP), 2006
[12] O.E.Langland, R.P.Langlais and J.W.Preece, "Principles of Dental
Imaging", Lippincott Williams & Wilkins, 2002.
[13] O.Molven, A.Halse, I.Fristad and M.Jankowski, " Periapical changes
following root-canal treatment observed 20-27 years postoperatively",
International Journal of Endodontic Research, vol: 35(9), pp: 784-90,
2002
[14] ImageJ , Image Processing and Analysis in Java official website
http://rsbweb.nih.gov/ij/.
[15] Allen, B. Wilkinson M. Parallel Programming, Techniques and
Applications Using Networked Workstations and Parallel Computers,
Pearson,2005.
[16] J.Poulist and M.Aubin, "Contrast Limited Adaptive Histogram
Equalization(CLAHE),http://radonc.ucsf.edu/research_group/jpouliot/Tu
torial/HU/Lesson7.htm, accessed on 4th June 2010.
[17] E.Pisano et al,"Contrast Limites Adaptive Histogram Equalization Image
Processing to Improve the Detection of Simulated Spiculations in Dense
Mammograms", Journal of Digital Imaging",Vol 11, pp: 193-200, 1998
@article{"International Journal of Medical, Medicine and Health Sciences:53226", author = "S.A.Ahmad and M.N.Taib and N.E.A.Khalid and R.Ahmad and H.Taib", title = "Performance of Compound Enhancement Algorithms on Dental Radiograph Images", abstract = "The purpose of this research is to compare the original
intra-oral digital dental radiograph images with images that are
enhanced using a combination of image processing algorithms. Intraoral
digital dental radiograph images are often noisy, blur edges and
low in contrast. A combination of sharpening and enhancement
method are used to overcome these problems. Three types of
proposed compound algorithms used are Sharp Adaptive Histogram
Equalization (SAHE), Sharp Median Adaptive Histogram
Equalization (SMAHE) and Sharp Contrast adaptive histogram
equalization (SCLAHE). This paper presents an initial study of the
perception of six dentists on the details of abnormal pathologies and
improvement of image quality in ten intra-oral radiographs. The
research focus on the detection of only three types of pathology
which is periapical radiolucency, widen periodontal ligament space
and loss of lamina dura. The overall result shows that SCLAHE-s
slightly improve the appearance of dental abnormalities- over the
original image and also outperform the other two proposed
compound algorithms.", keywords = "intra-oral dental radiograph, histogram equalization,sharpening, CLAHE.", volume = "5", number = "2", pages = "41-6", }
