Abstract. Watersheds are very powerful for image segmentation, and seeded watersheds have shown to be useful for object detection in images of cells in vitro. This paper shows that if cells are imaged over time, segmentation results from a previous time frame can be used as seeds for watershed segmentation of the current time frame. The seeds from the previous frame are combined with morphological seeds from the current frame, and over-segmentation is reduced by rule-based merging, propagating labels from one time-frame to the next. Thus, watershed segmentation is used for segmentation as well as tracking of cells over time. The described algorithm was tested on neural stem/progenitor cells imaged using time-lapse microscopy. Tracking results agreed to 71% to manual tracking results. The results were also compared to tracking based on solving the assignment problem using a modified version of the auction algorithm.

Detection and localization of fluorescent signals in relation to other subcellular structures is an important task in various biological studies. Many methods for analysis of fluorescence microscopy image data are limited to 2D. As cells are in fact 3D structures, there is a growing need for robust methods for analysis of 3D data. This article presents an approach for detecting point-like fluorescent signals and analyzing their subnuclear position. Cell nuclei are delineated using marker-controlled (seeded) 3D watershed segmentation. User-defined object and background seeds are given as input, and gradient information defines merging and splitting criteria. Point-like signals are detected using a modified stable wave detector and localized in relation to the nuclear membrane using distance shells. The method was applied to a set of biological data studying the localization of Smad2-Smad4 protein complexes in relation to the nuclear membrane. Smad complexes appear as early as 1 min after stimulation while the highest signal concentration is observed 45 min after stimulation, followed by a concentration decrease. The robust 3D signal detection and concentration measures obtained using the proposed method agree with previous observations while also revealing new information regarding the complex formation. ' 2008 International Society for Advancement of Cytometry Key terms3D image analysis; fluorescence signal segmentation; subcellular positioning; Smad detection THE subcellular location of a protein or a protein complex is directly related to its function. This makes the detection and localization of protein complexes in relation to other subcellular structures an important task in biological studies. Highly specific staining methods and fluorescent markers emitting light at different wavelengths together with fluorescence microscopy allow for detailed studies of the spatial distribution and localization of biomolecules. Exact localization of fluorescent signals can be challenging as biological samples are three-dimensional (3D), and thus, the signals are often spread across a 3D volume.There are a number of techniques for 3D biological fluorescence microscopy, and the performance of these methods has been studied in recent literature (1). Wide-field fluorescence microscopy is one such commonly used technique. Images acquired by a wide-field microscope provide clear lateral information, but limited axial information. Methods like deconvolution are used with wide-field microscopes to reduce this problem (2). Another approach is to use techniques like spot scanning/laser scanning or spinning disk confocal microscopy. Use of such confocal techniques for volumetric (3D) imaging is the most common approach in many biological applications today.Localization of fluorescence signals from in situ detected proteins within their cellular compartments has been an active research field over the years (3,4). A comparison of image analysis-based methods for determination of intracellular location of fluorescent-labeled ...

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Robust detection and localization of biomolecules inside cells is of great importance to better understand the functions related to them. Fluorescence microscopy and specific staining methods make biomolecules appear as point-like signals on image data, often acquired in 3D. Visual detection of such point-like signals can be time consuming and problematic if the 3D images are large, containing many, sometimes overlapping, signals. This sets a demand for robust automated methods for accurate detection of signals in 3D fluorescence microscopy. We propose a new 3D point-source signal detection method that is based on Fourier series. The method consists of two parts, a detector, which is a cosine filter to enhance the point-like signals, and a verifier, which is a sine filter to validate the result from the detector. Compared to conventional methods, our method shows better robustness to noise and good ability to resolve signals that are spatially close. Tests on image data show that the method has equivalent accuracy in signal detection in comparison to visual detection by experts. The proposed method can be used as an efficient point-like signal detection tool for various types of biological 3D image data. ' 2009 International Society for Advancement of Cytometry

Controlling the complexity of software applications is an essential part of the software development process as it directly affects maintenance activities such as reusability, understandability, modifiability and testability. However, as stated by Tom DeMarco "You cannot control what you cannot measure". Thus, over the years many complexity metrics have been proposed with the intention of controlling and minimizing the complexity associated with software. However, majority of these proposed complexity metrics are based on only one aspect of complexity. The CB measure introduced by Chhillar and Bhasin is one metric which relies on a number of complexity factors to decide on the complexity of a program. However, italso has some shortcomings and can be further improved. Thus, this paper attempts to propose some additional complexity factors that the CB measure has not considered, to further improve it. The paper also presents an extensive coverage about the software complexity metrics proposed in the literature.

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Rapid bone substitutes manufacturing is highly important due to a vast number casualties are stepped to the society as a result of mainly traffic accidents, natural disasters and civil wars. Though casualties are grouped into several categories, a considerable number of patients is fallen into the bone associated injuries. It is also notable that especially the traffic related accidents and natural disasters may occur in populated regions. Due to financial reasons all the hospitals in the developing countries cannot maintain sophisticated scanning equipments along with their software solutions. Therefore having a lightweight software solution that facilitates bone profiling will be beneficial for patients and it also helps surgeons to prepare a care plan depending on the disorder. However, the artificial tools that are inserted to the human body can vary upon the injury. Hence, they should be highly customizable. Though computerized 3D modeling started around two decades ago, a few tools are available to assist surgeons in such situations. The available applications and techniques have limited functionalities thus, the manufactured bone grafts may not perfectly be suited to the lesion or injury. In this paper we propose a minimally invasive procedures to model bone grafts. In which, quality control methods for noise removals and 3D data compression mechanisms are coupled to the software solution that runs even on typical personal computer systems. The end result of the 3D modeled bone can be employed to extract the cavity, clip regions of interest and even to test the manufactured bone graft before the surgical procedure. Thereby, the process of manufacturing the prosthetic and the clinical procedures will be efficient and reliable.

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