Presenting a new method to investigate the treatment process of lesions and tooth bone loss in a period of time

Number of pages: 104 File Format: word File Code: 31020
Year: 2012 University Degree: Master's degree Category: Computer Engineering
  • Part of the Content
  • Contents & Resources
  • Summary of Presenting a new method to investigate the treatment process of lesions and tooth bone loss in a period of time

    Master's Thesis in Computer Engineering (Artificial Intelligence)

    Abstract

    Various image processing techniques are widely used in dentistry. Among these applications, we can mention the identification based on dental images, the detection of dental caries, as well as the identification and examination of dental lesions, which mainly occur around the end of the tooth root. Also, by using image processing techniques, the dentist can be helped in diagnosing dental lesions so that the treatment process can be carried out with more quality and accuracy. Failure to treat these lesions may lead to serious damage to the gum and tooth tissue and decrease the bone mass of the tooth. In this treatise, a new method is presented based on the image characteristics of teeth undergoing root canal treatment, for dividing and separating teeth from each other, as well as determining the position of the end of the root and finally identifying dental lesions. The experimental results of testing the proposed algorithm on the existing images show that despite the low quality of the images, the presented algorithm is effective in separating the teeth from each other and determining the location of the end of the root and detecting the area with bone lesions. Chapter 1 - Introduction The tooth is composed of a hard structure that surrounds living soft tissue. Figure 1-1 shows the tooth structure. The central soft part of the tooth is called the pulp and in popular terms, the nerve, which includes cells, blood vessels and nerves. Most of the structure of the tooth is dentin, inside the dentin there is a space that contains nerve and capillary network, which is called the pulp. The pulp branches from the main branches of nerves and vessels at the end of the root and reaches this space through the channels in the root. Sensation, nutrition, dentin formation and microbial defense are the functions of the pulp. This soft tissue may be infected by microbes and transmit the infection to the bone around the root. In such cases, all this tissue is removed by the dentist, the pulp chamber and canals are cleaned and filled in an optimal way. If the root of the tooth is not treated, the disease will eventually lead to tooth extraction or loss. Complications of non-treatment include abscess, pain, severe infection and eventually tooth loss. If the infected, inflamed and painful pulp is not removed, and in other words, the treatment is not done, the pain usually increases and bothers the patient. The spread of the infection to the bone at the end of the root causes a cyst or abscess at the end of the root. Its basis is the self-rectifying tube. The tube is installed in such a way that it has the most maneuverability. The focal point size of the tube is small. The timer of the device works by clock or electronic.

    (Images are available in the main file)

    Dental X-ray units Dental X-ray units are used for imaging teeth, anatomy of a single tooth (i.e. crown, neck and root) and dental problems such as caries in adult and pediatric patients as well as for orthodontic planning and evaluation. Three types of imaging can be performed: intraoral radiography, panoramic and cephalometric. In intraoral radiography, the film is placed inside the patient's mouth for bitewing, periapical and occlusal imaging. Radiographic images of the bite wing, crown and upper third of the roots of the upper and lower teeth show. In periapical radiography, the entire structure of the tooth, including the root, is imaged on one film, and the upper and lower jaws are imaged on separate films. Alcosal radiographs[1] show the surface of small and large middle teeth. In panoramic radiography, images of the jaw and face area are obtained using a beam turner and an external film cassette. The dental arch is then displayed as an ellipse in a single image. Panoramic units are used to prepare local radiographic images of the dental structure. 1-2-1-1-2-1 Cephalometric radiography [2] Cephalometric radiography is a type of radiographic equipment that is used to prepare a standard image of the skull. This technology was introduced in 1913 by two German and American researchers. Its most important advantage is that the images produced by this device can be compared at different times and places.. Also, the position of the head and its distance to the source of radiation and film is always fixed. Cephalometric images can be prepared laterally or posterior-anteriorly, which are mainly used in orthodontics and maxillofacial surgery. Cephalometric radiography or skull view is used to obtain images of the entire skull or a desired area. Figure 1-1 shows an example of cephalometric images. Cephalometric studies are used to evaluate growth and determine orthodontic treatment plans or prostheses. Some panoramic and cephalometric units can perform cross-sectional tomography to prepare multi-layer cross-sectional images of the upper and lower jaws.

    (images are available in the main file)

    1-2-2- Digital dental radiography systems

    Digital radiographic systems, which are also called digital dental imaging systems, to prepare computerized images for intraoral radiography. They are used as an alternative to X-ray films in conventional dentistry. Direct digital imaging and image processing provides the possibility of displaying multiple images, reducing exposure times, and eliminating the time required for film exposure and proofing. Digital imaging can be used for endodontic applications, planning and evaluating implants, and other procedures on teeth that require multiple images.

    Digital systems that allow immediate viewing of images without the use of film from an intraoral sensor or imaging screen, an x-ray system, hardware and software. A computer to process the image and a printer to prepare the printed version have been formed. In systems that use an intraoral sensor (CCD[3]). During imaging, the sensor is placed inside the patient's mouth and electronically connected to the computer system. This sensor detects x-rays and converts them directly into electrical signals. Then the digital image data is sent to the computer system for processing. In other examples, the sensor includes a rate-earth resonator plate coupled to a CCD array by optical fiber. This array sends the analog signal to the display processing unit. Where this signal is converted pixel by pixel into an image, the intraoral sensor is encased in a resistant material to protect the CCD electronics from moisture. In order to control health and prevent infection during examinations, disposable polyethylene covers have been installed. Another type of digital dental imaging system uses imaging plates instead of an intraoral sensor. Thin and wireless imaging plates, like conventional intraoral films, are fixed in the patient's mouth and cover the same diagnostic area as the films. After the radiation is emitted, the imaging screen is placed in a laser scanner that digitizes the image to apply changes on the computer screen. Imaging plates can be used repeatedly and disposable plastic clips that cover the plates during radiography are used to prevent the transfer of contamination between patients. The digital imaging system can be used in conjunction with a conventional intraoral radiography unit. A compatible personal computer with the appropriate software is used to apply changes to images and perform image processing effects that include zooming, image rotation, edge sharpening, high-quality color, multi-image rendering, brightness and contrast adjustments, and measuring distances and angles. Also, some systems provide the possibility of managing data sets, and the resulting radiographic images can be stored and recycled in a standard file format, and a printed version of it can be prepared by a video printer. It can be done in such a way that the complete condition of the tooth and its surrounding tissues is shown. Figure 2-2 shows an example of a periapical radiographic image. The reason for naming it as periapical is that it shows the presence of a lesion around the tip of the root well, but this technique is also used to examine the bone edges around the tooth as well as interdental caries. Of course, in checking decay, the working method is Byte Wing.

  • Contents & References of Presenting a new method to investigate the treatment process of lesions and tooth bone loss in a period of time

    List:

    The first chapter: Introduction

    1-1- General. 2

    1-2- An introduction to dental radiology. 3

    1-2-1- Cephalometric radiography 4

    1-2-2- Dental radiography digital systems 5

    1-2-3- Periapical radiography 7

    1-2-4- Panoramic radiography (Panorex) 8

    1-2-5- Intraoral cameras 9

    1-3- Definition of the problem. 9

    1-4- A look at the thesis chapters. 11

    1-5- Summary. 12

    The second chapter: Research background

    2-1- Introduction. 14

    2-2- Existing methods for separating and segmenting dental images. 16

    2-2-1 Saeed et al.'s method 17

    2-2-2- Anil Jin and Heng Chen's method 26

    Title

    2-2-3- Fang Din and Bak Huai's method 28

    2-2-4- Method of Vijayakumari et al. 36

    2-3- Available methods for examining and diagnosing dental lesions. 40

    2-3-1- methods of examination and treatment of tooth root resorption 40

    2-3-2 methods of examination and diagnosis of dental lesions around the end of the root 41

    2-3-2-1 Mol and Venn method. 41

    2-3-2-2 Jang and Lee method. 44

    2-4- Summary. 46

    The third chapter: Research method

    3-1- Introduction. 48

    3-2- Preprocessing and improving the quality of images. 49

    3-2-1-Synthetic filter.  52

    3-3- Division and separation of teeth 52

    3-3-1- Storage mapping histogram 53

    3-3-2- Rotation angle estimation: 55

    3-4- Identifying the end of the tooth root: 61

    3-5- Detection of bone lesions around the root of the tooth: 64

    3-6- Summary. 65

    Chapter Four: Experiments and results

    4-1- Introduction. 67

    4-2- Checking the results of the pre-processing stage. 68

    Title                                                                                                                                                                                                                               Page 4-3-4-3-4-3-1- Results and tests of the teeth segmentation stage 73

    4-3-2- Evaluation of teeth segmentation stages 76

    4-4- The results of the tooth root end detection algorithm: 82

    4-5- Assessment and diagnosis of bone lesions at the end of the tooth root. 84

    4-6- Summary. 86

    The fifth chapter: Conclusions and suggestions

     

    5-1- Conclusions and suggestions for future work 88

     

    List of sources. 90

    Source:

    [1] root treatment. (n.d.). Retrieved 1 14 2013, from roshd: http://daneshnameh.roshd.ir/mavara/mavara-print.php?page=Darman+Risheh

    [2] Medical Engineering Articles (n.d.). Retrieved 12 26 2012, from : http://bmeiaut.salamatblog.ir/1389/05/03/57/

    [3] D. Kim, B. Lee, "Quantitative Analysis of Endodontic Treatment for Periapical Lesions in Intraoral Radiographs," 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering, Taichung, Taiwan, pp. 352-355, June 2009.

    [4] N.M.R, A. E. (1980). Imaging of intact biological systems. Philos trans R soc lond (biol) , 471-481.

    [5] Taylor Z.D., Rahul D., Bennett D. (2012, sep). THz medical imaging: in vivo hydration sensing. IEEE transaction on terahertz science and technology, 1(1).

    [6] Townsend, D. W. (2008). Dual-modality imaging: combining anatomy and function. Journal of Nucl Med.

    [7] E. Said, D. Nassar, G. Fahmy, and H. Ammar. (2006, June). Teeth segmentation in digitized dental x-ray films using mathematical morphology. IEEE Trans. Inf. Security Forensics, 1, 178–189. [8] R. C. Gonzales, R. E. Woods. (2002). Digital Image Processing. Upper Saddle River: Prentice-Hall. [9] J. Zhou and M. Abdel Mottaleb. (2005, November). A content-based system for human identification based on bitewing dental x-ray images. Pattern Recognition, 38(11), 2132-2142.

    [10] A. Jain and H. Chen. (2004, July). Matching of dental x-ray images for human identification. pattern recognitionPattern Recognition, 37(7), 1519-1532. [11] Vo Phong Dinh, Le Bac–Hoai. (2008). Dental radiographs Segmentation Based on Tooth Anatomy. IEEE International Conference on Research, Innovation and Vision for the Future in Computing & Communication Technologies.

    [12] Tooth, (n.d.). Retrieved 10 8 2012, from Dr Sharif Weblog: http://doctorsharif.blogfa.com/category/1

    [13] B. Vijayakumari, G. UIaganathan, A. Banumath. (2011). An effective shape extraction algorithm for dental radiographs using contour information. international journal of computer science and technology. [14] Levander E, Bajka R, Malmgren O. (1998). Early Radiographic Diagnosis of Apical Root Resorption During Orthodontic Treatment: A Study Of Maxillary Incisors. Eur J Orthod, 57-63. [15] Evelise Ono, et al. (2011, March). Evaluation of simulated external root resorptions with digital radiography and digital subtraction radiography. American journal of orthodontics and dentofacial orthopedics, 324-333. [16] A Mol, P. F. (1991, April). Application of Digital Image Analysis in Dental Radiography for the Description of Periapical Bone Lesions: A Preliminary Study. IEEE transactions on biomedical engineering, 38(4). [17] D. Kim, B. Lee. (2009). Quantitative Analysis of Endodontic Treatment for Periapical Lesions in Intraoral Radiographs; IEEE International Conference on Bioinformatics and Bioengineering, Taichung, Taiwan.352-355.

    [18] Goebe, P. M. (2006). Noise estimation and multiresolution reconstruction for dental panoramic X-ray images. Diploma thesis.

    [19] Image quality. (n.d.). Retrieved from mpas: http://www.mpas.ir/mri/31-image-quality.html

    [20] Homomorphic filtering. (n.d.). Retrieved 6 26 2012 from wikipedia: http://en.wikipedia.org/wiki/Homomorphic_filtering

    [21] R. Khanna and W. Shen. (1994). Automated fingerprint identification system (AFIS) benchmarking using the National Institute of Standards and Technology (NIST) special database. in Proc. Inst. Elect. Electron.Eng. 28th Annu. Int. Carnahan Conf., Albuquerque, NM. 188–194.

    [22] D. E. Nassar. A prototype automatic dental identification system (ADIS). (2000) M.Sc. dissertation, Dept. Elect. Comput. Eng. West Virginia Univ. Morgantown, Apr. 2001. [23] S. White and M. Pharoah. Oral Radiology Principles and Interpretation, 4th ed. St. Louis, MO: Mosby, 2000.

    [24] Yuniarti, A., Zainal Arifin, A., Yudhi Wijaya, A., & Nurul Khotimah, W. (2012). An Age Estimation Method to Panoramic Radiographs from Indonesian Individuals. TELKOMNIKA (Telecommunication, Computing, Electronics and Control), 10(1), 137-146

    [25] P. Maragos and R. W. Schafer. (1990) Morphological systems for multidimensional signal processing. Proc. IEEE. 78(4). 690–710

    [26] K. Held, E. R. Kops, B. J. Krause, W. M. Wells, and R. Kikinis. (1997, December). Markov random field segmentation of brain MR images. IEEE Trans. Med. Image. 16(6). 878–886

    [27] Lin, P. L., Huang, P. Y., & Huang, P. W. (2012). An automatic lesion detection method for dental x-ray images by segmentation using variational level set. In ICMLC (pp. 1821-1825). [28] S. Shiffman, G. D. Rubin, and S. Naples. (2000, November). Medical image segmentation using analysis of isolable-contour maps. IEEE Trans. Med. Image. 19(1). 1064–1074 [29] V. Grau, A. U. J. Mewes, M. Alcaniz, R. Kikinis, and S. K. Warfield. (2004, April). Improved watershed transform for medical image segmentation using prior information. IEEE Trans. Med. Image. 23(4) 447–458. [30] L.G. Brown. (1992). A survey of image registration techniques. ACM Comput. Surveys 24(4). pp325

    [31] J.J Charles, L.I. Kuncheva, B. Wells, I.S. Lim. (2006, September). An evaluation measure of image segmentation based on object centers. in: Proc. Of the international conference on image analysis and recognition, Portugal, 18-20, [32] G. Jonasson, G.

Presenting a new method to investigate the treatment process of lesions and tooth bone loss in a period of time