Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Perhitungan Dan Pemisahan Sel Darah Putih Berdasarkan Centroid Dengan Menggunakan Metode Multi Pass Voting Dan K-Means Pada Citra Sel Acute Leukemia

Leukemia merupakan salah satu jenis penyakit berbahaya yang dapat menyebabkan kematian. Salah satu tipe leukemia adalah jenis Acute leukemia yang terdiri dari ALL (Acute Lymphoblastic Leukemia) dan AML (Acute Myeloid Leukemia). Identifikasi tercepat yang dapat dilakukan terhadap penyakit ini adalah melakukan perhitungan dan analisa jenis sel darah putih. Namun perhitungan dan analisa jenis sel darah putih yang dilakukan secara manual masih terbatas oleh waktu. Oleh karena itu perlu dilakukan proses secara otomatis untuk mendapatkan hasil yang lebih cepat dan akurat. Pada penelitian sebelumnya, proses perhitungan yang dilakukan secara otomatis pada citra sel Acute Leukemia masih terdapat kendala, yaitu keberadaan sel bersentuhan dan penggunaan fitur geometri yang belum bisa menghasilkan perhitungan yang akurat, karena bentuk sel yang beragam. Penelitian ini mengusulkan sebuah metode untuk perhitungan dan pemisahan sel darah putih yang bersentuhan pada citra sel Acute Leukemia dengan metode Multi Pass Voting (MPV) berdasarkan deteksi seed (centroid) dan metode K-Means. Deteksi awal region sel darah putih menggunakan deteksi tepi canny. Dilanjutkan deteksi seed (centroid) menggunakan metode Multi Pass Voting dan perhitungan jumlah sel darah putih dilakukan berdasarkan centroid yang dihasilkan. Keberadaan sel bersentuhan akan dipisahkan dengan metode K-Means, dengan penentuan centroid awal berdasarkan hasil dari metode Multi Pass Voting. Dari hasil evaluasi yang dilakukan terhadap 40 citra pada dataset Acute Leukemia, metode yang diusulkan melakukan perhitungan berdasarkan centroid dengan baik dan memisahkan sel bersentuhan menjadi sel-sel tunggal. Hasil akurasi perhitungan jumlah sel darah putih yaitu sebesar 98,6%. ================================================================= Leukemia is one of the dangerous diseases that can cause death. One of the types of leukemia is acute leukemia that in-cludes ALL (Acute Lymphoblastic Leukemia) and AML (Acute Myeloid Leukemia). The fastest identification against this disease can be done by computing and analysing white blood cell types. However, the manual counting and identification of the white blood cell types are still limited by time. Therefore, automatic counting process is necessary to be conducted in order to get the results more quickly and accurately. Previous studies showed that automatic counting process in the image of Acute Leukemia cells faced some obstacles, the existence of touching cell and the implementation of geometry feature that cannot produce an accurate counting. It is because the shapes of the cell are various. This study proposed a method for the counting of white blood cells and the separation of touching cells on Acute Leukemia cells image by using Multi Pass Voting method (MPV) based on seed detection (centroid) and K-Means method. Initial segmentation used for separating foreground and background area is canny edge detection. The next stage is seed detection (centroid) using Multi Pass Voting method. The counting of white blood cells is based on the results of the centroid produced. The existence of the touching cells are separated using K-Means method, the determination of the initial centroid is based on the results of the Multi Pass Voting method. Based on the evaluation results of 40 images of Acute Leukemia dataset, the proposed method is capable to properly compute based on the centroid. It is also able to separate the touching cell into a single cell. The accuracy of the white blood cell counting result is about 98,6%

Medical image segmentation using edge-based active contours.

The main purpose of image segmentation using active contours is to extract the object of interest in images based on textural or boundary information. Active contour methods have been widely used in image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may limit the accuracy of any segmentation method formulated using active contour models. This thesis develops new methods for segmentation of medical images based on the active contour models. Three different approaches are pursued: The first chapter proposes a novel external force that integrates gradient vector flow (GVF) field forces and balloon forces based on a weighting factor computed according to local image features. The proposed external force reduces noise sensitivity, improves performance over weak edges and allows initialization with a single manually selected point. The next chapter proposes a level set method that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the images gradient vector flow field and the evolving contours normal. Finally, chapter 5 presents a framework that is capable of segmenting the cytoplasm of each individual cell and can address the problem of segmenting overlapping cervical cells using edge-based active contours. The main goal of our methodology is to provide significantly fully segmented cells with high accuracy segmentation results. All of the proposed methods are then evaluated for segmentation of various regions in real MRI and CT slices, X-ray images and cervical cell images. Evaluation results show that the proposed method leads to more accurate boundary detection results than other edge-based active contour methods (snake and level-set), particularly around weak edges