scholarly journals Volumetric Tooth Wear Measurement of Scraper Conveyor Sprocket Using Shape from Focus-Based Method

2019 ◽  
Vol 9 (6) ◽  
pp. 1084 ◽  
Author(s):  
Hua Ding ◽  
Yinchuan Liu ◽  
Jiancheng Liu
Keyword(s):  
Median Filter ◽  
Normal Operator ◽  
Tooth Wear ◽  
Wear Volume ◽  
Wear Measurement ◽  
Engineering Technique ◽  
Scraper Conveyor ◽  
Window Selection ◽  
Occurrence Matrix ◽  
Shape From Focus

Volumetric tooth wear measurement is important to assess the life of scraper conveyor sprocket. A shape from focus-based method is used to measure scraper conveyor sprocket tooth wear. This method reduces the complexity of the process and improves the accuracy and efficiency of existing methods. A prototype set of sequence images taken by the camera facing the sprocket teeth is collected by controlling the fabricated track movement. In this method, a normal distribution operator image filtering is employed to improve the accuracy of an evaluation function value calculation. In order to detect noisy pixels, a normal operator is used, which involves with using a median filter to retain as much of the original image information as possible. In addition, an adaptive evaluation window selection method is proposed to address the difficulty associated with identifying an appropriate evaluation window to calculate the focused evaluation value. The shape and size of the evaluation window are autonomously determined using the correlation value of the grey scale co-occurrence matrix generated from the measured pixels’ neighbourhood pixels. A reverse engineering technique is used to quantitatively verify the shape volume recovery accuracy of different evaluation windows. The test results demonstrate that the proposed method can effectively measure sprocket teeth wear volume with an accuracy up to 97.23%.

scholarly journals Effective Approach to Classify and Segment Retinal Hemorrhage Using ANFIS and Particle Swarm Optimization

2018 ◽  
Vol 27 (4) ◽  
pp. 681-697
Author(s):  
Lawrence Livingston Godlin Atlas ◽  
Kumar Parasuraman
Keyword(s):  
Particle Swarm Optimization ◽  
Median Filter ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Retinal Hemorrhage ◽  
Scale Invariant ◽  
Swarm Optimization ◽  
Segmented Images ◽  
Occurrence Matrix ◽  
Scale Invariant Feature

Abstract The main objective of this study is to progress the structure and segment the images from hemorrhage recognition in retinal fundus images in ostensible. The abnormal bleeding of blood vessels in the retina which is the membrane in the back of the eye is called retinal hemorrhage. The image folders are deliberated, and the filter technique is utilized to decrease the images specifically adaptive median filter in our suggested proposal. Gray level co-occurrence matrix (GLCM), grey level run length matrix (GLRLM) and Scale invariant feature transform (SIFT) feature skills are present after filtrating the feature withdrawal. After this, the organization technique is performed, specifically artificial neural network with fuzzy interface system (ANFIS) method; with the help of this organization, exaggerated and non-affected images are categorized. Affected hemorrhage images are transpired for segmentation procedure, and in this exertion, threshold optimization is measured with numerous optimization methods; on the basis of this, particle swarm optimization is accomplished in improved manner. Consequently, the segmented images are projected, and the sensitivity is great when associating with accurateness and specificity in the MATLAB platform.

scholarly journals Klasifikasi Kanker Paru Paru menggunakan Naïve Bayes dengan Variasi Filter dan Ekstraksi Ciri GLCM

2021 ◽  
Vol 11 (2) ◽  
pp. 256
Author(s):  
Mohtar Yunianto ◽  
Soeparmi Soeparmi ◽  
Cari Cari ◽  
Fuad Anwar ◽  
Delta Nur Septianingsih ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Median Filter ◽  
Naïve Bayes ◽  
Gray Level ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Otsu Thresholding ◽  
Low Pass ◽  
Occurrence Matrix ◽  
High Pass

<p class="AbstractText">Telah berhasil dilakukan klasifikasi kanker paru-paru dari 120 data citra CT Scan. Pada penelitian, proses preposisi dimulai dengan variasi filtering yaitu low pass filter, median filter, dan high pass filter. Segmentasi yang digunakan yaitu Otsu Thresholding yang kemudian teksturnya akan diekstraksi menggunakan fitur Gray Level Co-occurrence Matrix (GLCM) dengan variasi arah sudut. Hasil dari ekstraksi GLCM dijadikan database yang akan menjadi dataset untuk pengklasifikasian citra menggunakan klasifikasi naïve bayes. Hasil dari penelitian dengan 12 buah variasi diperoleh hasil variasi terbaik adalah median filter dengan arah sudut GLCM 0° menunjukkan tingkat akurasi yang paling tinggi sebesar 88,33 %.</p>

scholarly journals Measurement of Tooth Wear by Means of Digital Impressions: An In-Vitro Evaluation of Three Intraoral Scanning Systems

2021 ◽  
Vol 11 (11) ◽  
pp. 5161
Author(s):  
Christina Kühne ◽  
Ulrich Lohbauer ◽  
Stefan Raith ◽  
Sven Reich
Keyword(s):  
Reference Method ◽  
In Vitro Study ◽  
Tooth Wear ◽  
Rank Test ◽  
Promising Alternative ◽  
Wear Facet ◽  
Wear Measurement ◽  
Signed Rank ◽  
Signed Rank Test

This in-vitro study aimed to investigate whether intraoral scanners (IOS) are suitable for wear measurement compared to optical profilometry (WLP). A zirconia cast representing the teeth (24–28) was fabricated. It was digitized six times using three different intraoral scanners, Cerec Omnicam AC (OC), Trios 3 (Tr3), and True Definition (TD). The scans were conducted at baseline (t0) and at three different stages of simulated wear (t1–t3), each at one wear-facet on FDI 26 and FDI 27. WLP was used as a reference method. Within each acquisition system, the maximum wear at each facet was analyzed by superimposing the STL data of t0 with t1–t3. A power analysis was performed (G*Power), and the Wilcoxon-signed-rank-test was used to evaluate whether there were statistically significant differences between the groups (Bonferroni corrected) (α = 0.05). At wear-facet FDI 27, differences from +4% t1 TD up to +19% t2 OC, corresponding to a metric value of 8 µm and 45 µm, were measured. At FDI 26 deviations between −2% t1 Tr3, and +10% OC and Tr3, were observed. Considering some limitations, the IOS are a promising alternative to wear measurement based on WLP due to its simple application to capture surface changes in a reasonable and quick way.

scholarly journals PERBAIKAN KONTRAS CITRA MAMMOGRAM PADA KLASIFIKASI KANKER PAYUDARA BERDASARKAN FITUR GRAY-LEVEL CO-OCCURRENCE MATRIX

2020 ◽  
Vol 3 (1) ◽  
pp. 46-51
Author(s):  
Febri Liantoni ◽  
Agus Santoso
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Median Filter ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Support Vector ◽  
Gray Level ◽  
Image Construction ◽  
Occurrence Matrix ◽  
Mammogram Images

In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram equalization are used to improve image quality. Feature extraction using Gray-Level Co-Occurrence Matrix (GLCM) with 5 features, entropy, correlation, contrast, homogeneity, and variance. The final step is the classification using Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM). Based on the hypotheses that have been tested and discussed, the accuracy for RBFNN is 86.27%, while the accuracy for SVM is 84.31%. This shows that the RBFNN method is better than SVM in distinguishing types of breast cancer. These results prove the process of improving image construction using histogram equalization and the median filter is useful in the classification process.

scholarly journals 3D Occlusal Tooth Wear Assessment in Presence of Limited Changes in Non-Occlusal Surfaces

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1033
Author(s):  
Nikolaos Gkantidis ◽  
Konstantinos Dritsas ◽  
Christos Katsaros ◽  
Demetrios Halazonetis ◽  
Yijin Ren
Keyword(s):  
Morphological Changes ◽  
Tooth Wear ◽  
Primary Outcome Measure ◽  
Wear Volume ◽  
Assessment Technique ◽  
Occlusal Surfaces ◽  
Dental Models ◽  
Tooth Type ◽  
Digital Dental Models

The study aimed to develop an accurate and convenient 3D occlusal tooth wear assessment technique, applicable when surfaces other than the occlusal undergo changes during the observation period. Various degrees of occlusal tooth wear were simulated in vitro on 18 molar and 18 premolar plaster teeth. Additionally, their buccal and lingual surfaces were gently grinded to induce superficial changes and digital dental models were generated. The grinded and the original tooth crowns were superimposed using six different 3D techniques (two reference areas with varying settings; gold standard: GS). Superimposition on intact structures provided the GS measurements. Tooth wear volume comprised the primary outcome measure. All techniques differed significantly to each other in their accuracy (p < 0.001). The technique of choice (CCD: complete crown with 30% estimated overlap of meshes) showed excellent agreement with the GS technique (median difference: 0.045, max: 0.219 mm3), no systematic error and sufficient reproducibility (max difference < 0.040 mm3). Tooth type, tooth alignment in the dental arches, and amount of tooth wear did not significantly affect the results of the CCD technique (p > 0.01). The suggested occlusal tooth wear assessment technique is straightforward and offers accurate outcomes when limited morphological changes occur on surfaces other than the occlusal.

scholarly journals Micro-Calcification Classification Analysis in Mammogram Images With aid of Hybrid Technique Analysis

2021 ◽  
Author(s):  
M.C. Shanker ◽  
M. Vadivel
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Median Filter ◽  
Support Vector ◽  
Hybrid Technique ◽  
Scale Invariant ◽  
Hu Moments ◽  
Automated Technique ◽  
Occurrence Matrix ◽  
Mammogram Images

Abstract The main cause of death in women is breast cancer. Early identification can contribute significantly to improving the survival rate. For diagnosis and accurate therapy automatic detection of micro-calcification is therefore essential. In the paper, an automated technique is utilized in the mammogram images according to their micro-calcification classification. The automated technique is working with the combination of Deep Belief Neural Network (DBNN) and Chimp Optimization Algorithm (COA). The proposed method is working with three phases such as pre-processing phase, feature extraction, and classification phase. In the pre-processing phase, a median filter is utilized to remove unwanted information from the images. In the feature extraction phase, Gray Level Co-Occurrence Matrix (GLCM), Scale-Invariant Feature Transform (SIFT), and Hu moments are utilized to extract essential features from the mammogram images. After that, the detection and classification are performed on the mammogram images according to their micro-calcifications with the utilization of the proposed advanced deep learning method. From the classification stage, the normal and abnormal images are identified from the images. The proposed method is implemented in the MATLAB platform and analyzed their statistical performances like accuracy, sensitivity, specificity, precision, recall, and F-measure. To evaluate the effectiveness of the proposed method this is compared with the existing method such as Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).

scholarly journals Klasifikasi Kelainan Pada Jantung Melalui Citra Iris Mata Menggunakan Fuzzy C-Means Sebagai Pengambil Fitur Iris Dan Klasifikasi Menggunakan Support Vector Machine

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Dian Candra rini Novitasari ◽  
Muhammad Fahrur Rozi ◽  
Rafika Veriani
Keyword(s):  
Support Vector Machine ◽  
Median Filter ◽  
Support Vector ◽  
Grey Level ◽  
Fuzzy C Means ◽  
Occurrence Matrix

Iridologi merupakan diagnosis sebuah iris mata yang merepresentasikan tanda-tanda seperti warna dan struktur dari iris sehingga didapatkan informasi tentang kesehatan seseorang. Penelitian ini tentang iridologi yang terkomputerisasi oleh sebuah sistem yang digunakan dalam mendeteksi keadaan jantung yang dirancang dengan langkah-langkah seperti pra-proseskonversi citra ari RGB menjadi Grayscale, penghapusan noise menggunakan median filter, pemangkasan, pengelompokan menggunakan Fuzzy C-Means (FCM), deteksi tepi menggunakan metode Canny dan diikuti fitur ekstraksi menggunakan Grey Level Co-occurrence Matrix (GLCM), serta klasifikasi menggunakan Support Vector Machine (SVM). Sampel iris pasien dalam keadaan normal dan tidak normal. Data iris pasien yang memiliki kelainan jantung sebanyak 20 citra. Hasil dari sistem deteksi kelainan Jantung melalui citra iris ini memiliki tingkat akurasi sebesar 75%.

scholarly journals Classification of Leukemia Image Using Genetic Based K-Nearest Neighbor (G-KNN)

2018 ◽  
Vol 7 (2) ◽  
pp. 113-117
Author(s):  
M. Bennet Rajesh ◽  
S. Sathiamoorthy
Keyword(s):  
Blood Cell ◽  
Nearest Neighbor ◽  
Median Filter ◽  
Diagnostic System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Initial Stage ◽  
Medical Diagnostic ◽  
Occurrence Matrix

In medical diagnostic system, classification of blood cell is more vigorous to identify the disease. The diseases which are connected with blood is alienated after the categorization of blood cell. Leukemia, a blood cancer that begins in bone marrow. Hence, it must be cured at initial stage and leads to death if left untreated. This paper introduces median filter for noise removing and Genetic based kNN for classification of Leukemia image datasets and features are extracted using gray-level co-occurrence matrix. The outcome of proposed genetic algorithm based kNN is compared with multilayer perceptron and support vector machine. The experimental outcomes evident that proposed combination performs better than the existing approach.

scholarly journals Evaluation of the effectiveness of the new tooth wear measurement parameters

2015 ◽  
Vol 48 (4) ◽  
pp. 284 ◽  
Author(s):  
Sang-Hak Lee ◽  
Shin-Eun Nam ◽  
Seung-Pyo Lee
Keyword(s):  
Tooth Wear ◽  
Wear Measurement

scholarly journals Recognition of Lung Tumor Area Based on Watershed Segmentation and CNN

2020 ◽  
Vol 9 (8) ◽  
pp. 20-24
Keyword(s):  
Ct Scan ◽  
Lung Tumor ◽  
Early Recognition ◽  
Early Stage ◽  
Median Filter ◽  
Input Image ◽  
Watershed Segmentation ◽  
Complex Wavelet Transform ◽  
Occurrence Matrix ◽  
Preprocessing Technique

Early recognition of tumor would assist in saving an enormous number of lives over the globe frequently. Study and remedy of lung tumor have been one of the greatest troubles faced by humans over the latest few decades. Effective recognition of lung tumor is a vital and crucial aspect of image processing. Several Segmentation methods were used to detect lung tumor at an early stage. An approach is presented in this paper to diagnose lung tumor from CT scan images. The input image (CT scan image) will be preprocessed initially using median filter to remove the noise. After applying preprocessing technique, the Dual-Tree Complex Wavelet Transform (DTCWT) segmentation technique is used for the edge detection. The Gray-Level Co-occurrence Matrix (GLCM) features are calculated based on the pixel values of the extracted image. These features can be compared with database images using Convolutional Neural Network (CNN) which facilitates in categorizing it as tumorous. After confirming that the affected area is tumorous, watershed segmentation algorithm is used to get the color features of the tumor.

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