Image segmentation with adaptive region growing based on a polynomial surface model

A new method for segmenting intensity images into smooth surface segments is presented. The main idea is to divide the image into flat, planar, convex, concave, and saddle patches that coincide as well as possible with meaningful object features in the image. Therefore, we propose an adaptive region growing algorithm based on low-degree polynomial fitting. The algorithm uses a new adaptive thresholding technique with the L∞ fitting cost as a segmentation criterion. The polynomial degree and the fitting error are automatically adapted during the region growing process. The main contribution is that the algorithm detects outliers and edges, distinguishes between strong and smooth intensity transitions and finds surface segments that are bent in a certain way. As a result, the surface segments corresponding to meaningful object features and the contours separating the surface segments coincide with real-image object edges. Moreover, the curvature-based surface shape information facilitates many tasks in image analysis, such as object recognition performed on the polynomial representation. The polynomial representation provides good image approximation while preserving all the necessary details of the objects in the reconstructed images. The method outperforms existing techniques when segmenting images of objects with diffuse reflecting surfaces

Automatic annotation of cellular data

Life scientists often need to count cells in microscopy images, which is very tedious and a time consuming task. Henceforth, automatic approaches can be a solution to this problem. Several works have been devised for this issue, but the majority of these approaches degrade their performance in case of cell overlapping. In this dissertation we propose a method to determine the position of macrophages and parasites in uorescence images of Leishmania-infected macrophages. The proposed strategy is mainly based on blob detection, clustering and separation using concave regions of the cells' contour. By carrying out a comparison with other approaches that also addressed this type of images, we concluded that the proposed methodology achieves better performance in the automatic annotation of Leishmania infections.A anotação de células é uma tarefa comum a diversas áreas da investigação biomédica. Normalmente, esta tarefa é realizada de forma manual, sendo um processo demorado, cansativo e propício a erros. Neste trabalho, focamos o nosso interesse na anotação de imagens de uorescência com infeções de Leishmania, que representa um destes casos. Leishmania são parasitas unicelulares que infectam mamíferos, sendo responsáveis por um conjunto de doenças conhecidas por leishmanioses. Leishmania usam vertebrados como hospedeiros residindo dentro dos seus macrófagos. Por conseguinte, um modelo adequado para o estudo destes parasitas é infectar in vitro culturas de macrófagos. A capacidade de sobrevivência/replicação da Leishmania nessas condições arti - ciais pode então ser avaliada por parâmetros, como, por exemplo, a percentagem de macrófagos infectados, o número médio de parasitas por macrófagos infectados e o índice de infeção. Essas métricas são geralmente determinadas pela contagem de parasitas e macrófagos ao microscópio. Ambos os tipos de células podem ser facilmente distinguidos com base no seu tamanho e cor, resultante de diferentes a nidades de corantes uorescentes. A passagem desta tarefa do microscópio para o computador já foi conseguida através de aplicações como o CellNote, contudo, apesar de mais fácil e interativa, a anotação continua a ser manual. A evolução da abordagem manual para um processo automático representa um passo natural e lógico, constituindo o principal objetivo deste trabalho. Para isto iniciámos a investigação pela revisão dos principais métodos de deteção e contagem celular. As características das imagens com infeções de Leishmania impossibilitam a utilização dos métodos estudados, de tal modo que optámos por desenvolver uma nova abordagem, capaz de lidar com as várias especi cidades destas imagens. Também durante o processo de revis ão de literatura analisámos os dois métodos previamente propostos para realizar a anotação automática de infeções de Leishmania. Estes revelaram um desempenho abaixo do requerido pelos parasitologistas, justi cando também o desenvolvimento de uma nova abordagem. Durante a concepção do sistema investigámos diversas técnicas de deteção celular, onde a deteção de blobs se destacou pelos resultados positivos. Para segmentar as regiões citoplasmáticas optámos pela utilização de algoritmos de clustering. Estes não foram capazes de solucionar casos em que existia sobreposição de estruturas celulares, motivando assim o método de separação desenvolvido. Este método baseia-se maioritariamente na análise de contorno, sendo as suas concavidades geradoras de separação entre citoplasmas. Através da combinação destas fases foi possível detetar macrófagos e parasitas com mais precisão. Para con rmar esta conclusão testámos não só a nossa abordagem mas também as duas abordagens previamente desenvolvidas para este problema. Os desempenhos alcançados evidenciam não só uma melhoria comparativamente às restantes abordagens como também mostram que a nossa abordagem assegura resultados satisfatórios comparativamente aos obtidos manualmente. Em suma, o trabalho desenvolvido produziu um sistema capaz de realizar a anotação automática de imagens de uorescência com infeções de Leishmania, tendo originado um artigo aceite para publicação na conferência International Conference on Image Analysis and Recognition (ICIAR) 2013

Cellular quantitative analysis of neuroblastoma tumor and splitting overlapping cells

© 2014 Tafavogh et al.; licensee BioMed Central Ltd. Background: Neuroblastoma Tumor (NT) is one of the most aggressive types of infant cancer. Essential to accurate diagnosis and prognosis is cellular quantitative analysis of the tumor. Counting enormous numbers of cells under an optical microscope is error-prone. There is therefore an urgent demand from pathologists for robust and automated cell counting systems. However, the main challenge in developing these systems is the inability of them to distinguish between overlapping cells and single cells, and to split the overlapping cells. We address this challenge in two stages by: 1) distinguishing overlapping cells from single cells using the morphological differences between them such as area, uniformity of diameters and cell concavity; and 2) splitting overlapping cells into single cells. We propose a novel approach by using the dominant concave regions of cells as markers to identify the overlap region. We then find the initial splitting points at the critical points of the concave regions by decomposing the concave regions into their components such as arcs, chords and edges, and the distance between the components is analyzed using the developed seed growing technique. Lastly, a shortest path determination approach is developed to determine the optimum splitting route between two candidate initial splitting points.Results: We compare the cell counting results of our system with those of a pathologist as the ground-truth. We also compare the system with three state-of-the-art methods, and the results of statistical tests show a significant improvement in the performance of our system compared to state-of-the-art methods. The F-measure obtained by our system is 88.70%. To evaluate the generalizability of our algorithm, we apply it to images of follicular lymphoma, which has similar histological regions to NT. Of the algorithms tested, our algorithm obtains the highest F-measure of 92.79%.Conclusion: We develop a novel overlapping cell splitting algorithm to enhance the cellular quantitative analysis of infant neuroblastoma. The performance of the proposed algorithm promises a reliable automated cell counting system for pathology laboratories. Moreover, the high performance obtained by our algorithm for images of follicular lymphoma demonstrates the generalization of the proposed algorithm for cancers with similar histological regions and histological structures

Automatic Leukemia Cell Counting using Iterative Distance Transform for Convex Sets

The calculation of white blood cells on the acute leukemia microscopic images is one of the stages in the diagnosis of Leukemia disease. The main constraint on calculating the number of white blood cells is the precision in the area of overlapping white blood cells. The research on the calculation of the number of white blood cells overlapping generally based on geometry. However, there was still a calculation error due to over segment or under segment. This paper proposed an Iterative Distance Transform for Convex Sets (IDTCS) method to determine the markers and calculate the number of overlapping white blood cells. Determination of marker was performed on every cell both in single and overlapping white blood cell area. In this study, there were tree stages: segmentation of white blood cells, marker detection and white blood cell count, and contour estimation of every white blood cell. The used data testing was microscopic acute leukemia image data of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloblastic Leukemia (AML). Based on the test results, Iterative Distance Transform for Convex Sets IDTCS method performs better than Distance Transform (DT) and Ultimate Erosion for Convex Sets (UECS) method

A New Approach for Realistic 3D Reconstruction of Planar Surfaces from Laser Scanning Data and Imagery Collected Onboard Modern Low-Cost Aerial Mapping Systems

Over the past few years, accurate 3D surface reconstruction using remotely-sensed data has been recognized as a prerequisite for different mapping, modelling, and monitoring applications. To fulfill the needs of these applications, necessary data are generally collected using various digital imaging systems. Among them, laser scanners have been acknowledged as a fast, accurate, and flexible technology for the acquisition of high density 3D spatial data. Despite their quick accessibility, the acquired 3D data using these systems does not provide semantic information about the nature of scanned surfaces. Hence, reliable processing techniques are employed to extract the required information for 3D surface reconstruction. Moreover, the extracted information from laser scanning data cannot be effectively utilized due to the lack of descriptive details. In order to provide a more realistic and accurate perception of the scanned scenes using laser scanning systems, a new approach for 3D reconstruction of planar surfaces is introduced in this paper. This approach aims to improve the interpretability of the extracted planar surfaces from laser scanning data using spectral information from overlapping imagery collected onboard modern low-cost aerial mapping systems, which are widely adopted nowadays. In this approach, the scanned planar surfaces using laser scanning systems are initially extracted through a novel segmentation procedure, and then textured using the acquired overlapping imagery. The implemented texturing technique, which intends to overcome the computational inefficiency of the previously-developed 3D reconstruction techniques, is performed in three steps. In the first step, the visibility of the extracted planar surfaces from laser scanning data within the collected images is investigated and a list of appropriate images for texturing each surface is established. Successively, an occlusion detection procedure is carried out to identify the occluded parts of these surfaces in the field of view of captured images. In the second step, visible/non-occluded parts of the planar surfaces are decomposed into segments that will be textured using individual images. Finally, a rendering procedure is accomplished to texture these parts using available images. Experimental results from overlapping laser scanning data and imagery collected onboard aerial mapping systems verify the feasibility of the proposed approach for efficient realistic 3D surface reconstruction