Spatial Condition in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs

 Dental panoramic radiographs heavily depend on the performance of the segmentation method due to the presence of unevenly illumination and low contrast of the images. Conditional Spatial Fuzzy C-mean (csFCM) Clustering have been proposed to achieve through the incorporation of the component and added in the FCM to cluster grouping. This algorithm directs with consideration conditioning variables that consider membership value. However, csFCM does not consider Intuitionistic Fuzzy Set to take final membership and final non-membership value into account, the effect does not wipe off the deviation by illumination and low contrast of the images completely for improvement to skip some scope. In this current paper, we introduced a new image segmentation method namely Conditional Spatial in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs. Our proposed method adds hesitation function aiming to settle the indication of the knowledge lack that belongs to the final membership function to get a better segmentation result. The experiment result shows this method achieves better segmentation performance with misclassification error (ME) and relative foreground area error (RAE) values are 4.77 and 4.27 respectively

Lokal Fuzzy Thresholding Berdasarkan Pengukuran Fuzzy Similarity Pada Interaktif Segmentasi Citra Panoramik Gigi

Dalam segmentasi citra, thresholding merupakan salah metode yang mudah dan sederhana untuk diimplementasikan. Pada citra panoramik gigi, penentuan global threshold masih kurang begitu optimal untuk diimplementasikan. Hal tersebut dikarenakan adanya factor penghambat seperti pencahayaan yang tidak merata dan citra yang kabur. Faktor-faktor tersebut  dapat menyebabkan histogram tidak bisa dipartisi dengan baik, sehingga akan berpengaruh pada hasil segmentasi. Pada penelitian ini diusulkan lokal fuzzy thresholding berdasarkan pengukuran fuzzy similarity pada interaktif segmentasi citra panoramik gigi. Metode yang diusulkan terdiri dari tiga tahapan utama, tahap pertama region splitting untuk mendapatkan lokal region. Tahap kedua adalah user marking untuk mendapat inisial seed background dan objek, Tahap terakhir adalah pengukuran fuzzy similarity pada setiap lokal region untuk mendapatkan nilai lokal threshold. Hasil uji coba pada citra panoramik gigi, metode yang diusulkan berhasil melakukan segmentasi dengan rata-rata missclasification error (ME) 5.47%

A Review on Automatic Detection of Dental Caries in Bitewing Radiography

Anticipation of more obtrusive helpful treatment modalities requires early caries determination which dental clinicians look amid ordinary practice. The essential visual investigation technique has halfway unwavering quality for recognizing noncavitated injuries, particularly on proximal surfaces. In this manner, dental specialists routinely favor bitewing radiographs as an extra technique for finding of the carious injuries. Numerous radiologic components can influence the capacity to precisely identify the carious sores, presentation parameters, kind of picture receptor, picture preparing, show framework, seeing conditions, and visual fantasies. Adjacent to these radiologic factors, different morphologic wonders, for example, pits and crevices; dental irregularities, for example, hypo-plastic pits and concavities; and obtained changes of dentition, for example, scraped spot and disintegration can mirror the presence of a carious sore. In this way, the result of a false positive analysis is the start of superfluous obtrusive remedial treatment. The dental specialist’s information about the elements mirroring the proximal carious sore recognized by bitewing radiography is essential for clinical practice to keep the patient out of these pointless medicines

SEPARATION OF OVERLAPPING OBJECT SEGMENTATION USING LEVEL SET WITH AUTOMATIC INITALIZATION ON DENTAL PANORAMIC RADIOGRAPH

To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%.  Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology

Inisialisasi Otomatis Metode Level Set untuk Segmentasi Objek Overlapping pada Citra Panorama Gigi

Penelitian tentang segmentasi gigi individu telah banyak dilakukan dan memperoleh hasil yang baik. Namun, ketika dihadapkan kepada gigi overlap maka hal ini menjadi sebuah tantangan. Untuk memisahkan dua gigi overlap, maka perlu mengekstrak objek overlap terlebih dahulu. Metode level set banyak digunakan untuk melakukan segmentasi objek overlap, namun memiliki kelemahan yaitu perlu didefinisikan inisial awal metode level set secara manual oleh pengguna. Dalam penelitian ini diusulkan strategi inisialisasi otomatis pada metode level set untuk melakukan segmentasi gigi overlap menggunakan Hierarchical Cluster Analysis (HCA) pada citra panorama gigi. Tahapan strategi yang diusulkan terdiri dari preprocessing dimana di dalamnya ada proses perbaikan, rotasi dan cropping citra, dilanjutkan proses inisialisasi otomatis menggunakan algoritma HCA , dan yang terakhir segmentasi menggunakan metode level set. Hasil evaluasi menunjukkan bahwa strategi yang diusulkan berhasil melakukan inisialisasi secara otomatis dengan akurasi 73%. Hasil evaluasi segmentasi objek overlap cukup memuaskan dengan rasio misclassification error  0,93% dan relative foreground area error 24%. Dari hasil evaluasi menunjukkan bahwa strategi yang diusulkan dapat melakukan inisialisasi otomatis dengan baik. Inisialisasi yang tepat menghasilkan segmentasi yang baik pada metode level set.AbstractIndividual teeth segmentation has done a lot of the recent research and obtained good results. When faced with overlapping teeth, this is quite challenging. To separate overlapping teeth, it is necessary to extract the overlapping object first. The level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. This research proposes an automatic initialization strategy for the level set method to segment overlapping teeth using Hierarchical Cluster Analysis on dental panoramic radiograph images. The proposed strategy stage consists of preprocessing where there are several processes of enhancement, rotation, and cropping of the image, Then the automatic initialization process uses the HCA algorithm and the last is segmentation using the level set method. The evaluation results show that the proposed strategy is successful in carrying out automatic initialization with an accuracy of 73%. The results of the overlap object segmentation evaluation are satisfactory with a misclassification error ratio of 0.93% and a relative foreground area error of 24%. The evaluation results show that the proposed strategy can carry out automated initialization well. Proper initialization results can perform good segmentation of the level set method

Use of texture feature maps for the refinement of Information derived from digital Intraoral radiographs of lytic and sclerotic lesions

The aim of this study was to examine whether additional digital intraoral radiography (DIR) image preprocessing based on textural description methods improves the recognition and differentiation of periapical lesions. (1) DIR image analysis protocols incorporating clustering with the k-means approach (CLU), texture features derived from co-occurrence matrices, first-order features (FOF), gray-tone difference matrices, run-length matrices (RLM), and local binary patterns, were used to transform DIR images derived from 161 input images into textural feature maps. These maps were used to determine the capacity of the DIR representation technique to yield information about the shape of a structure, its pattern, and adequate tissue contrast. The effectiveness of the textural feature maps with regard to detection of lesions was revealed by two radiologists independently with consecutive interrater agreement. (2) High sensitivity and specificity in the recognition of radiological features of lytic lesions, i.e., radiodensity, border definition, and tissue contrast, was accomplished by CLU, FOF energy, and RLM. Detection of sclerotic lesions was refined with the use of RLM. FOF texture contributed substantially to the high sensitivity of diagnosis of sclerotic lesions. (3) Specific DIR texture-based methods markedly increased the sensitivity of the DIR technique. Therefore, application of textural feature mapping constitutes a promising diagnostic tool for improving recognition of dimension and possibly internal structure of the periapical lesions

Inisialisasi Otomatis Metode Level Set untuk Segmentasi Objek Overlapping pada Citra Panorama Gigi

Ekstraksi fitur dari gigi yang sudah disegmentasi mempunyai tantangan ketika dihadapkan pada gigi yang overlap. Segmentasi pada gigi yang overlap menggunakan algoritma terkini masih menghasilkan satu objek sehingga perlu dilakukan pemisahan yang diawali mendapatkan objek overlap terlebih dahulu. Metode level set digunakan untuk melakukan segmentasi objek overlap, namun memiliki kelemahan yaitu perlu didefinisikan inisial awal secara manual oleh pengguna yang cukup melelahkan jika diterapkan pada jumlah data yang banyak. Dalam penelitian ini diusulkan strategi inisialisasi otomatis pada metode level set untuk melakukan segmentasi gigi yang overlap pada citra panorama gigi. Proses melakukan segmentasi gigi yang overlap diawali dengan pendefinisian region of interest (ROI) kemudian dibagi menjadi 4 tahap yaitu: preprocessing, segmentasi objek gigi, segmentasi objek overlap menggunakan metode level set dengan inisialisasi awal otomatis, pemotongan dua gigi overlap. Metode yang digunakan dalam melakukan inisialisasi otomatis pada metode level set memanfaatkan fitur intensitas dan geometri. Inisialisasi objek overlap dengan fitur intensitas menggunakan metode multi-thresholding HCA (hierarchical cluster analysis) dengan menggunakan threshold ketiga. Sedangkan pada fitur geometri dapat memanfaatkan orientasi dari objek overlap yaitu berorientasi vertikal. Dari hasil evaluasi yang diperoleh dapat ditarik beberapa kesimpulan diantaranya: strategi gabungan menggunakan metode multi-thresholding HCA, operasi morfologi erosi, dan seleksi objek berdasarkan fitur geometri, berhasil dalam menentukan lokasi objek overlap dengan baik dengan akurasi sebesar 87%. Segmentasi objek overlap menggunakan metode level set mampu mencapai hasil yang baik dengan nilai ME dan RAE sebesar 0,9342% dan 24,283%. Strategi gabungan yang terdiri dari penentuan titik potong dan operasi aljabar persamaan garis yang melewati dua titik dapat memisahkan dua gigi yang overlap secara interaktif pada proses segmentasi gigi dengan cukup baik terbukti memperoleh nilai ME dan RAE masing-masing sebesar 6,77% dan 13,55%, daripada menggunakan metode otomatis yang memperoleh 16,41% dan 52,14%. Sistem yang telah dibangun diharapkan mampu membantu melakukan segmentasi terhadap citra gigi yang overlap untuk penilaian estimasi usia manusia melalui citra gigi dalam bidang Odontologi Forensik. ================================================================================================================================= Feature extraction from segmented teeth has challenges when faced with overlapping teeth. Segmentation of overlapping teeth using the latest algorithm still produces one object, so it is necessary to separate that beginning by obtaining the overlapping object. The level set method is used to segment overlap objects. Nevertheless, it has a limitation where the initialization was done manually by the user which is exhausting if applied to the large amount of data. In this study, we proposed an automatic initialization strategy of the level set method to segment the overlapping teeth on dental panoramic radiographs. The process of segmenting overlapping teeth was begun by defining the Region of Interest (ROI), then was divided into 4 stages, i.e. pre-processing, dental objects segmentation, overlapping object segmentation using a level set method with automatic initialization, and separating two overlapping teeth. The method used in automatic initialization of the level set method is facilitated by intensity and geometry features. The initialization of overlap objects with intensity features was conducted by using the multi-thresholding HCA (Hierarchical Cluster Analysis) method with the third threshold. Whereas the geometry features can utilize the orientation of overlap objects, namely vertically oriented. The proposed automatic initialization strategy that consisted of the HCA multi-thresholding method, erosion morphological operations, and object selection based on geometry features were succeed in determining the location of overlapping objects. The level set method was able to initialize overlapping objects properly with accuracy of 87%. The overlap object segmentation using the level set method is able to achieve good results with ME and RAE values of 0.9342% and 24.283%. The combined strategy which consists in determining the intersection point and algebraic operation of the equation of a line passing through two points can separate two teeth that overlap interactively in the process of tooth segmentation. This is evidenced by the ME and RAE values of 6.77% and 13.55%, instead of using the automatic method which obtained 16.41% and 52.14%. The system that has been built is expected to be able to help the segmentation of the overlapping dental images for the assessment of human age estimation through dental images in the field of Forensic Odontology

Deep Learning and Quantum-computing Based Optimization in Medical Imaging and Power Dispatcing

兵庫県立大学大学院202