IMPLEMENTASI ACTIVE SHAPE MODEL UNTUK SEGMENTASI CITRA PINDAI TULANG

Segmentasi citra merupakan suatu proses yang sangat penting untuk mendapatkan suatu pola dari citra, agar selanjutnya hasil segmentasi dapat dikembangkan untuk mengenal pola kemudian membagi citra menjadi beberapa region ataupun untuk proses klasifikasi. Pada penelitian ini melakukan segmentasi citra dari hasil bone scan dan bertujuan untuk mendefinisikan citra bone scan kedalam beberapa bagian. Segmentasi dari hasil bone scan ini dapat menjadi dasar acuan untuk melakukan analisis atau diagnostik. Untuk keperluan penelitian, citra dibagi ke dalam 4 (empat) bagian terdefinisi, yaitu kepala dan tulang belakang, lengan atas dan selangka, dada, dan panggul dan paha atas. Model dibangun dengan menggunakan Constrained Local Model, lalu proses penyesuaian (fitting) dengan menggunakan algoritma optimasi Active Shape Model. Proses dari fitting menghasilkan rata-rata nilai final error dari 3-fold-cross-validation sebesar 0,0446

Search of dynamic magnetic resonance images using active shape model

This paper focuses on segmenting dynamic magnetic resonance (MR) images of the human heart stored in a database. Heart MR images are dynamic, as the size and shape of a person?s heart vary in time. Active Shape Model segmentation was used to segment dynamic images of the heart. The input data is a points file of a set of random points. This method is economical since points of all the frames need not be placed in the points file for search. Segmented dynamic images of an organ such as a heart would help physicians to better understand certain medical conditions. The novelty of this approach is that it allows automatic segmentation of thousands of dynamic MR images in the database, visualization of the shape variation and retrieval of similar cases of interest from the image database

Observasi Citra Paru Menggunakan Active Shape Model Sebagai Opini Teknologi Medis

Paru merupakan salah satu organ tubuh yang berfungsi untuk proses pernapasan. Sesuai dengan fungsinya, kondisi dan kesehatan organ paru sangat di butuh dalam kehidupan manusia. Untuk mengetahui kondisi kesehatan organ paru dibutuhkan upaya observasi kondisi paru secara rutin. Tujuan dari penelitian ini tidak lain adalah mengembangkan perangkat lunak untuk dijadikan desain prototipe observasi citra paru sebagai opini teknologi medis menggunakan active shape model. Upaya untuk melakukan observasi kondisi organ paru dalam penelitian ini menggunakan data citra paru yang dihasilkan dari perangkat Computed Tomography Scan (CT-Scan) 2D. Data citra paru tersebut di evaluasi dengan proses segmentasi dengan metode active shape model. Hasil dari penelitian ini berupa perangkat lunak prototype observasi citra paru manusia sebagai opini teknologi medis menggunakan active shape model

Active shape model unleashed with multi-scale local appearance

We focus on optimising the Active Shape Model (ASM) with several extensions. The modification is threefold. First, we tackle the over-constraint problem and obtain an optimal shape with minimum energy considering both the shape prior and the salience of local features, based on statistical theory: a compact closed form solution to the optimal shape is deduced. Second, we enhance the ASM searching method by modelling and removing the variations of local appearance presented in the training data. Third, we speed up the convergence of shape fitting by integrating information from multi-scale local features simultaneously. Experiments show significant improvement brought by these modifications, i.e., optimal shape against standard relaxation methods dealing with inadequate training samples; enhanced searching method against standard gradient descent methods in searching accuracy; multi-scale local features against popular coarse-to-fine strategies in convergence speed

Improved echocardiography segmentation using active shape model and optical flow

Heart disease is one of the most dangerous diseases that threaten human life. The doctor uses echocardiography to analyze heart disease. The result of echocardiography test is a video that shows the movement of the heart rate. The result of echocardiography test indicates whether the patient’s heart is normal or not by identifying a heart cavity area. Commonly it is determined by a doctor based on his own accuracy and experience. Therefore, many methods to do heart segmentation is appearing. But, the methods are a bit slow and less precise. Thus, a system that can help the doctor to analyze it better is needed. This research will develop a system that can analyze the heart rate-motion and automatically measure heart cavity area better than the existing method. This paper proposes an improved system for cardiac segmentation using median high boost filter to increase image quality, followed by the use of an active shape model and optical flow. The segmentation of the heart rate-motion and auto measurement of the heart cavity area is expected to help the doctor to analyze the condition of the patient with better accuracy. Experimental result validated our approach

Automatic landmarking for building biological shape models

We present a new method for automatic landmark extraction from the contours of biological specimens. Our ultimate goal is to enable automatic identification of biological specimens in photographs and drawings held in a database. We propose to use active appearance models for visual indexing of both photographs and drawings. Automatic landmark extraction will assist us in building the models. We describe the results of using our method on drawings and photographs of examples of diatoms, and present an active shape model built using automatically extracted data

A Framework of Vertebra Segmentation Using the Active Shape Model-Based Approach

We propose a medical image segmentation approach based on the Active Shape Model theory. We apply this method for cervical vertebra detection. The main advantage of this approach is the application of a statistical model created after a training stage. Thus, the knowledge and interaction of the domain expert intervene in this approach. Our application allows the use of two different models, that is, a global one (with several vertebrae) and a local one (with a single vertebra). Two modes of segmentation are also proposed: manual and semiautomatic. For the manual mode, only two points are selected by the user on a given image. The first point needs to be close to the lower anterior corner of the last vertebra and the second near the upper anterior corner of the first vertebra. These two points are required to initialize the segmentation process. We propose to use the Harris corner detector combined with three successive filters to carry out the semiautomatic process. The results obtained on a large set of X-ray images are very promising

Sistem Pengukuran Lebar Cortical Bone Berbasis Active Shape Model pada Citra Panorama Gigi

Active Shape Model (ASM) adalah suatu metode yang dapat digunakan untuk mendapatkan tepi dari suatu objek dalam suatu citra. Dengan menempatkan titik-titik yang disebut sebagai landmark point sebagai repersentasi kontur, ASM dapat menemukan bentuk umum dari suatu objek dalam citra. Paper ini mengusulkan suatu sistem yang mampu secara otomatis mengukur lebar cortical bone dengan menggunakan metode berbasis ASM. Pengukuran dilakukan pada boundary hasil fitting antara model statistik yang dihasilkan oleh metode ASM dengan objek dalam citra. Dalam paper ini juga diteliti berapa jumlah yang optimal titik-titik sampel dalam membentuk suatu shape dari objek cortical bone. Dalam ujicoba terhadap 100 citra panorama gigi diperoleh jumlah titik yang optimal dalam training set adalah 50 titik. Sedangkan pembandingan hasil pengukuran dengan sistem ini terhadap hasil pengukuran secara manual mampu mencapai korelasi 90 %. Keyword: Active Shape Model (ASM), Pengukuran Lebar Cortical Bone, Citra Panorama Gigi

Active shape model precision analysis of vehicle detection in 3D lidar point clouds

LiDAR systems are frequently used for driver assistance systems. The minimal distance to other objects and the exact pose of a vehicle is important for ego movement prediction. Therefore, in this work, we extract the poses of vehicles from LiDAR point clouds. To this end, we measure them with LiDAR, segment the vehicle points and extract the pose. Further, we analyze the influence of LiDAR resolutions on the pose extraction by active shape models (ASM) and by the center of bounding boxes combined with the principal component analysis (BC-PCA). © Authors 2019