scholarly journals Tooth Position Determination by Automatic Cutting and Marking of Dental Panoramic X-ray Film in Medical Image Processing

2021 ◽  
Vol 11 (24) ◽  
pp. 11904
Author(s):  
Yen-Cheng Huang ◽  
Chiung-An Chen ◽  
Tsung-Yi Chen ◽  
He-Sheng Chou ◽  
Wei-Chi Lin ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Automatic Segmentation ◽  
Histogram Equalization ◽  
Feature Points ◽  
X Ray ◽  
Tooth Position ◽  
Positioning Method ◽  
Tooth Number ◽  
The Difference

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%.

scholarly journals Enhancement of Satellite Imagesusing Image Sharpening Technique

2019 ◽  
Vol 8 (6S2) ◽  
pp. 351-354
Keyword(s):  
Image Processing ◽  
Color Image ◽  
Histogram Equalization ◽  
Grayscale Image ◽  
Original Image ◽  
High Quality ◽  
Wide Range ◽  
Equalization Technique ◽  
The Difference

In many image processing applications, a wide range of image enhancement techniques are being proposed. Many of these techniques demanda lot of critical and advance steps, but the resultingimage perception is not satisfactory. This paper proposes a novel sharpening method which is being experimented with additional steps. In the first step, the color image is transformed into grayscale image, then edge detection process is applied using Laplacian technique. Then deduct this image from the original image. The resulting image is as expected; After performing the enhancement process,the high quality of the image can be indicated using the Tenengrad criterion. The resulting image manifested the difference in certain areas, the dimension and the depth as well. Histogram equalization technique can also be applied to change the images color.

scholarly journals Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur

2020 ◽  
Vol 5 (2) ◽  
pp. 53-60 ◽  
Author(s):  
Shoffan Saifullah
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Watershed Transform ◽  
Adaptive Histogram Equalization

Image processing dapat diterapkan dalam proses deteksi embrio telur. Proses deteksi embrio telur dilakukan dengan menggunakan proses segmentasi, yang membagi citra sesuai dengan daerah yang dibagi. Proses ini memerlukan perbaikan citra yang diproses untuk memperoleh hasil optimal. Penelitian ini akan menganalisis deteksi embrio telur berdasarkan image processing dengan image enhancement dan konsep segmentasi menggunakan metode watershed transform. Image enhacement pada preprocessing dalam perbaikan citra menggunakan kombinasi metode Contrast Limited Adaptive Histogram Equalization (CLAHE) dan Histogram Equalization (HE). Citra grayscale telur diperbaiki dengan menggunakan metode CLAHE, dan hasilnya diproses kembali dengan menggunakan HE. Hasil perbaikan citra menunjukkan bahwa metode kombinasi CLAHE-HE memberikan gambar secara jelas daerah objek citra telur yang memiliki embrio. Proses segmentasi dengan menggunakan konversi citra ke citra hitam putih dan segmentasi watershed mampu menunjukkan secara jelas objek telur ayam yang memiliki embrio. Hasil segmentasi mampu membagi daerah telur memiliki embrio secara nyata dan akurat dengan persentase sebesar  » 98%.

Joint HIS Color Space Based Image Enhancement under Uneven Illumination

2013 ◽  
Vol 718-720 ◽  
pp. 2232-2236
Author(s):  
Rui Xu Guo ◽  
Le Tian Zhang
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
High Performance ◽  
Color Space ◽  
Histogram Equalization ◽  
Homomorphic Filtering ◽  
Novel Algorithm

In this paper, we present a novel algorithm for uneven illumination image processing based on HIS color space and joint color space. Compared with many existing algorithms of image enhancement for the uneven illumination image, the proposed method have high performance compared with Histogram Equalization, Homomorphic filtering and Retinex. Some experiments are implemented to testify this conclusion.

Study on Terminal Identification Enhancement Method of Track and Field Using Digital X-Ray Photography Images

2014 ◽  
Vol 602-605 ◽  
pp. 2084-2088
Author(s):  
Xiao Lang Hu
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Boundary Value ◽  
Reconstruction Algorithm ◽  
Track And Field ◽  
Variation Principle ◽  
X Ray ◽  
Enhancement Technology ◽  
Recognition Function ◽  
Enhancement Method

Due to the development of image segmentation and reconstruction technology, it provides a larger space for development of the image enhancement technology. Under the promoting of the image processing calculation, the capture and recognition function of digital X-ray photography technology are stronger, and the image processing precision is higher. Based on the variation principle, this paper uses the function approximation to improve the X-ray photography image processing technology, and obtains the new boundary value reconstruction condition of X-ray photography. In order to verify the effectiveness and reliability of the mathematical model and algorithm of the boundary value reconstruction, this paper uses MATLAB software and C language to debug the algorithm, and realizes the digital color rendering for the images at the terminal of track and field, obtains the image reconstruction algorithm under different boundary values. It provides a new computer method for the research on image enhancement technology.

Research on the Image Enhancement Algorithm of Pointer Instrument under Inadequate Light

2014 ◽  
Vol 615 ◽  
pp. 248-254 ◽  
Author(s):  
Lu Zhang ◽  
Jin Lin Zhang ◽  
Ting Rui ◽  
Yue Wang ◽  
Yan Nan Wang
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Traditional Method ◽  
Histogram Equalization ◽  
Light Conditions ◽  
Low Light ◽  
Homomorphic Filtering ◽  
The Impact ◽  
Image Details ◽  
Enhancement Algorithm

For image processing, the recognition of pointer instrument’s reading by computer vision highly depends on brightness. An image enhancement algorithm based on homomorphic filtering and histogram equalization is proposed in order to reduce the impact of low-light conditions on images of pointer instrument. It combines the methods of spatial with frequency domain, which enhances the image contrast and highlights the image details as well. Compared with the traditional method, the experiments show that the proposed method can eliminate the effect of inadequate light and also perform well in enhancing the image quality.

scholarly journals An Algorithmic Approach towards Chest X-Ray Image Enhancement by Global Histogram Equalization

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
G. Raghavendra Prasad
Keyword(s):  
Medical Imaging ◽  
Image Enhancement ◽  
General Class ◽  
Bone Structure ◽  
Histogram Equalization ◽  
Algorithmic Approach ◽  
X Ray ◽  
Digital Chest ◽  
Chest X Ray ◽  
Color Depth

In medical imaging, the scope of image enhancement is highly challenging. Here digital chest x –ray image are taken in a spatial domain and enhancement of the image is done through histogram equalization method. Histogram equalization is a specific case of the more general class of histogram methods. Histogram Equalization works the best when applied to images with much higher color depth like continuos data or 16 bit gray scale images. In particular, the method can lead to better views of bone structure in X-ray images that are either over or under exposed. An algorithm is proposed to enhance the chest x-ray images using Global Histogram Equalization.

scholarly journals Identification of Lung Cancer Using a Back Propagation Neural Network

2015 ◽  
Vol 16 (1) ◽  
pp. 91
Author(s):  
Bambang Guruh Irianto ◽  
Mohamad Ridha Mak'ruf ◽  
Dyah Titisari
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Median Filter ◽  
Back Propagation ◽  
Lung Cancer Screening ◽  
Histogram Equalization ◽  
Training Image ◽  
Normal Lung ◽  
Image Size ◽  
X Ray

Reading image of lung cancer screening well-known as X-ray by practitioners are sometimes subjective. This research tried to create software that can detect lung cancer as a comparison of the work of medical practitioners using artificial neural networks (ANN), with X-ray movies taken from the tool diagnostic radiography (DR) stored in the compact disc. The dependent variable observation in this study is the identification of DR X-ray image size of 1024 x 1024 pixels. A total of 10 images X-ray which has been observed by the physician radiology. With 5 images X-ray normal and 5 images lung cancer. In this study, the image processing is done through three stages: neighborhood averaging, median filter and histogram equalization. The result of these features are grouped in normal categories. From test results stating the truth 80%. To facilitate the user in the lung disease pattern recognition. GUI applications design using MATLAB. We use some form of image processing which includes form training andtesting. The best parameters obtained from this research include learning rate=0.3, the number of hidden layer=30 and tolerance error=10-8. From the results obtained by the level of accuracy of the training image of normal lung, lung cancer in a row is 80%. Overall the level of accuracy of the results is 80%.

scholarly journals The Eye Retinal Blood Vessel Bonding By Image Processing

2019 ◽  
Vol 8 (3) ◽  
pp. 6848-6851
Keyword(s):  
Image Processing ◽  
Blood Vessel ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Retinal Blood Vessel ◽  
Contrast Method ◽  
Equalization Method

The method Histogram equalization is common in image enhancement. Using histogram to contrast the entire image and reduce noise .but we are using histogram equalization method remove the noise on entire image. But in some applications this is not suitable. Using contrast method we perform on small regions where our needed. The clip and block side method we will enhance the image. The contrast should be enhanced in this paper. Here we are used algorithm based on algorithm we should find the quality of the vessel bonding

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