Implementasi Metode Watershed Transformation dalam Segmentasi Tulisan Aksara Bali Berbasis Histogram

Aksara Bali merupakan tulisan yang dimiliki oleh agama hindu, seiring dengan perkembangan jaman Aksara Bali kurang diminati oleh kalangan muda, untuk itu kita perlu melestarikan kembali Aksara Bali. Untuk mengenali tulisan Aksara Bali harus dapat mengetahui tiap-tiap karakter yang terdapat pada tulisan Aksara Bali. Bagaimana mendapatkan tiap-tiap karakter pada tulisan Aksara Bali, dapat dilakukan dengan menggunakan beberapa metode segmentasi salah satunya adalah metode Watershed Transformation.Watershed Transformation merupakan sebuah metode dengan membedakan objek satu dengan objek yang lainnya dengan menggunakan garis pemisah seperti air. Jika sudah dapat garis pemisah antara objek satu dengan objek lainnya dilakukan pelabelan nilai tiap anggota kemudian dilakukan pemotongan berdasarkan tiap karakter. Melalui metode Watershed Transformation dibuatkan sebuah aplikasi yang dapat melakukan segmentasi tulisan Aksara Bali. Dari hasil pengujian yang dilakukan masih terdapat beberapa kesalahan dalam melakukan segmentasi tulisan Aksara Bali. Sehingga diperlukan proses preprocessing yang lebih baik untuk mendapatkan kualitas gambar yang lebih baik sebeum di segmentasi

Segmentation of Sedimentary Grain in Electron Microscopy Image

This paper describes a novel method developed for the segmentation of sedimentary grains in electron microscopy images. The algorithm utilizes the approach of region splitting and merging. In the splitting stage, the marker-based watershed segmentation is used. In the merging phase, the typical characteristics of grains in electron microscopy images are exploited for proposing special metrics, which are then used during the merging stage to obtain a correct grain segmentation. The metrics are based on the typical intensity changes on the grain borders and the compact shape of grains. The experimental part describes the optimal setting of parameter in the splitting stage and the overall results of the proposed algorithm tested on available database of grains. The results show that the proposed technique fulfills the requirements of its intended application

A Novel Model of Image Segmentation Based on Watershed Algorithm

A novel model of image segmentation based on watershed method is proposed in this paper. To prevent the oversegmentation of traditional watershed, our proposed algorithm has five stages. Firstly, the morphological reconstruction is applied to smooth the flat area and preserve the edge of the image. Secondly, multiscale morphological gradient is used to avoid the thickening and merging of the edges. Thirdly, for contrast enhancement, the top/bottom hat transformation is used. Fourthly, the morphological gradient of an image is modified by imposing regional minima at the location of both the internal and the external markers. Finally, a weighted function is used to combine the top/bottom hat transformation algorithm and the markers algorithm to get the new algorithm. The experimental results show the superiority of the new algorithm in terms of suppression over-segmentation

Computer Tomography 3D Edge Detection Comparative for Metrology Applications

AbstractThe CT process for metrology applications is very complex because has many factors that influence the loss of accuracy during CT measurements. One of the most critical is the edge detection also called surface extraction or image segmentation, which is the process of surface formation from the CT‘s volume data that represents a grey value corresponding to the mass attenuation coefficient of the object material. This paper presents different edge detection methods commonly used in areas like machine and computer vision and they are analyzed as an alternative to the common methods used in CT for metrology applications. Each method is described and analyzed separately in order to highlight its advantages and disadvantages from a metrological point of view. An experimental comparative between two of them is also shown

Individual Tooth Image Segmentation with Correcting of Specular Reflections

In this study, an efficient removal algorithm for specular reflections in a tooth color image is proposed to minimize the artefact interrupting color image segmentation. The pixel values of RGB color channels are initially reversed to emphasize the features in reflective regions, and then those regions are automatically detected by utilizing perceptron artificial neural network model and those prominent intensities are corrected by applying a smoothing spatial filter. After correcting specular reflection regions, multiple seeds in the tooth candidates are selected to find the regional minima and MCWA(Marker-Controlled Watershed Algorithm) is applied to delineate the individual tooth region in a CCD tooth color image. Therefore, the accuracy in segmentation for separating tooth regions can be drastically improved with removing specular reflections due to the illumination effectope

Image segmentation algorithms on female pelvic ultrasound images

The processing and analysis of structures presented in images has been one of the areas of Computational Vision with greater potential and applicability. The main goal of the researchers of this area has been the development of new computational methodologies to study the behaviour of structures in images. The mentioned image-based analysis is very important in many domains. For example, in Medicine, the information obtained by the proposed automatic analysis is crucial to understand the functioning and the behaviour of organs, and thus to assist medical doctors. A faithful simulation of organs is extremely important to improve the accuracy of medical virtual systems, as of the human body and of computational surgical simulators and robotic surgery. In this work, several algorithms of image segmentation are evaluated on ultrasound images acquired from female pelvic cavity

Semi-automatic algorithm for construction of the left ventricular area variation curve over a complete cardiac cycle

<p>Abstract</p> <p>Background</p> <p>Two-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve.</p> <p>Methods</p> <p>This work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles.</p> <p>Results</p> <p>The proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The method's intra- and inter-operator variabilities are also evaluated.</p> <p>Conclusions</p> <p>The proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle.</p

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed

Vision-based inspection of PCB soldering defects

Vision-based inspection of printed circuit board (PCB) soldering defects was studied for preparing feature data and classifying the overall PCB soldering defects on a PCB prototype into different classes. The image data of overall PCB soldering defects on a PCB prototype was developed using an image sensor camera. Image data augmentation was conducted to enhance the dataset volume. Image pre-processing included image resizing, image colour conversion, and image denoising. Watershed-based image segmentation was performed in the image post-processing to segmented images; then, feature extraction was conducted using curvelet transform to prepare image feature data. The feature data as the statistical data include kurtosis, contrast, energy, homogeneity, and variance. These data were analysed, and the percentage difference of mean values of statistical data between image classes was calculated. Kurtosis had the highest percentage difference among the statistical data. In the comparison of the mean values, kurtosis obtained 4.97% difference for the class of good and medium condition; 17.02% difference for the good and bad condition; and 12.08% difference for the bad and medium condition. Through this analysis, kurtosis is considered more reliable data for the machine-learning based classification in this project. The extracted data can be applied in future studies to classify overall solder joint defects on a PCB prototype by artificial neural network in machine learning classification

Automated detection of aortic annulus sizing based on decision level fusion

Aortic valve disease occurs due to calcification on the area of leaflets and it is progressive over time. Surgical Aortic Valve Replacement (SAVR) can be performed to treat the patient. However, due to invasive procedure of SAVR, a new method known as Transcatheter Aortic Valve Implantation (TAVI) has been introduced, where a synthetic catheter is placed within the patient’s heart valve. Traditionally, aortic annulus sizing procedure requires manual measurement of scanned images acquired from different imaging modalities which are Computed Tomographic (CT) and echocardiogram where both of the modalities produce inconsistency in measuring the aortic annulus yet able to produce different parameters which lead to accurate measurement. In this research, the image processing techniques of CT scan and echocardiogram images are done separately in order to obtain the aortic annulus size. Intensity adjustment and median filter are applied to CT scan image pre-processing, Watershed Transformation associated with the morphological operation has been used to perform the aortic annulus segmentation while image resizing and wavelet denoising method have been performed in echocardiogram image pre-processing followed by the implementation of Otsu N-clustering and morphological operation method for object segmentation. Then, Euclidean distance formula is applied to measure the distance between two points that indicates the diameter of the aortic annulus. Finally, a decision fusion technique based on the mathematical statistic approach has been applied to fuse the measured annulus size obtained from both modalities. Results affirmed the approach’s ability to achieve accurate annulus measurements when the final results are compared with the ground truth. In addition, the application of non-probabilistic estimation on the decision level fusion approach which does not required the dataset training produces fast computational time and helps in determining the optimal size of new aortic valve to be implemented in human heart