Image Analysis & Stereology

Ahead of print section

Current issue (table of contents)

    Lucie Kubínová
    Dear colleagues, Let me wish you and our Image Analysis & Stereology Journal all the best in 2016. It is great honour for me to be in charge of the International Society for Stereology (ISS). I would like to thank Eric Pirard for all his tremendous work and effort he has put into ISS activities in past years, together with all members of the ISS Board. I am glad Eric is willing to help me with ISS as „Immediate Past President“. I also rely on close collaboration with Ida Eržen and Marko Kreft, the Editors-in-Chief of Image Analysis & Stereology (IAS), IAS Editorial Board and future ISS Board. I appreciate long-term cooperation between the Image Analysis & Stereology Journal and the International Society for Stereology and I am looking forward to broaden our relationships in the future. As a President of ISS I plan to evoke new activities which would help to make ISS vivid and useful to scientific community. We are planning new courses on stereology, image analysis and related topics run in cooperation with ISS with reduced fee for ISS members, competion for the best PhD thesis using stereology and/or image analysis, ISS history mapping, etc. I welcome your further suggestions and comments. I would like to cordially invite you to become members of the International Society for Stereology ( and to take part in new activities organized by ISS, such as:Special Session „3D Image Analysis and Stereology in Fluorescence Microscopy“ at ISBI 2016 (in Prague, see table discussion on „Stereology and 3D image analysis in microscopy“ to be held at the 16th European Microscopy Congress in Lyon (EMC 2016, see is my intention to involve young people and fresh ideas in the activities of ISS and in further improvement of IAS. Please contact me at Looking forward to our collaboration, Lucie KubínováPresident of IASDepartment of BiomathematicsInstitute of PhysiologyCzech Academy of SciencesPrague, Czech Republic

    Ana Isabel Gomez, Marcos Cruz, Luis Manuel Cruz-Orive
    The estimator of planar curve length based on intersection counting with a square grid, called the Buffon-Steinhaus estimator, is simple, design unbiased and efficient. However, the prediction of its error variance from a single grid superimposition is a non trivial problem. A previously published predictor is checked here by means of repeated Monte Carlo superimpositions of a curve onto a square grid, with isotropic uniform randomness relative to each other. Nine curvilinear features (namely flattened DNA molecule projections) were considered, and complete data are shown for two of them. Automatization required image processing to transform the original tiff image of each curve into a polygonal approximation consisting of between 180 and 416 straight line segments or ‘links’ for the different curves. The performance of the variance prediction formula proved to be satisfactory for practical use (at least for the curves studied).

    Dhanya S Pankaj, Rama Rao Nidamanuri
    The 3D modeling pipeline involves registration of partially overlapping 3D scans of an object. The automatic pairwise coarse alignment of partially overlapping 3D images is generally performed using 3D feature matching. The transformation estimation from matched features generally requires robust estimation due to the presence of outliers. RANSAC is a method of choice in problems where model estimation is to be done from data samples containing outliers. The number of RANSAC iterations depends on the number of data points and inliers to the model. Convergence of RANSAC can be very slow in the case of large number of outliers. This paper presents a novel algorithm for the 3D registration task which provides more accurate results in lesser computational time compared to RANSAC. The proposed algorithm is also compared against the existing modifications of RANSAC for 3D pairwise registration. The results indicate that the proposed algorithm tends to obtain the best 3D transformation matrix in lesser time compared to the other algorithms.

    Kazeem Oyeyemi Oyebode, Jules R. Tapamo
    Graph cut segmentation approach provides a platform for segmenting images in a globally optimised fashion. The graph cut energy function includes a parameter that adjusts its data term and smoothness term relative to each other. However, one of the key challenges in graph cut segmentation is finding a suitable parameter value that suits a given segmentation. A suitable parameter value is desirable in order to avoid image oversegmentation or under-segmentation. To address the problem of trial and error in manual parameter selection, we propose an intuitive and adaptive parameter selection for cell segmentation using graph cut. The greyscale image of the cell is logarithmically transformed to shrink the dynamic range of foreground pixels in order to extract the boundaries of cells. The extracted cell boundary dynamically adjusts and contextualises the parameter value of the graph cut, countering its shrink bias. Experiments suggest that the proposed model outperforms previous cell segmentation approaches.

    Rostam Affendi Hamzah, Haidi Ibrahim, Anwar Hasni Abu Hassan
    This paper presents a new method of pixel based stereo matching algorithm using illumination control. The state of the art algorithm for absolute difference (AD) works fast, but only precise at low texture areas. Besides, it is sensitive to radiometric distortions (i.e., contrast or brightness) and discontinuity areas. To overcome the problem, this paper proposes an algorithm that utilizes an illumination control to enhance the image quality of absolute difference (AD) matching. Thus, pixel intensities at this step are more consistent, especially at the object boundaries. Then, the gradient difference value is added to empower the reduction of the radiometric errors. The gradient characteristics are known for its robustness with regard to the radiometric errors. The experimental results demonstrate that the proposed algorithm performs much better when using a standard benchmarking dataset from the Middlebury Stereo Vision dataset. The main contribution of this work is a reduction of discontinuity errors that leads to a significant enhancement on matching quality and accuracy of disparity maps.

    Enrico Vezzetti, Domenico Speranza, Federica Marcolin, Giulia Fracastoro
    The aim of this work is to automatically diagnose and formalize prenatal cleft lip with representative key points and identify the type of defect (unilateral, bilateral, right, or left) in three-dimensional ultrasonography (3D US). Geometry has been used as a framework for describing facial shapes and curvatures. Then, descriptors coming from this field are employed for identifying the typical key points of the defect and its dimensions. The descriptive accuracy of these descriptors has allowed us to automatically extract reference points, quantitative distances, labial profiles, and to provide information about facial asymmetry. Eighteen foetal faces, ten of healthy foetuses and eight with different types of cleft lips, have been obtained through a Voluson system and used for testing the algorithm. Cleft lip has been diagnosed and correctly characterized in all cases. Transverse and cranio-caudal length of the cleft have been computed and upper lip profile has been automatically extract to have a visual quantification of the overall labial defect. The asymmetry information obtained is consistent with the defect. This algorithm has been designed to support practitioners in identifying and classifying cleft lips. The gained results have shown that geometry might be a proper tool for describing faces and for diagnosis.