Marcin Kociolek

Survey statistics of automated segmentations applied to optical imaging of mammalian cells

BackgroundThe goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements.MethodsWe define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories.ResultsThe survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue.ConclusionsThe novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.

Model Based Approach for Melanoma Segmentation

is no suitable golden standard for assessment and comparison of segmentation methods applied to skin lesions images. Thus there is a need for development of image analysis techniques that satisfy at least subjective criteria defined by dermatologists. We present a model based approach for melanocytic image segmentation as a tool to improve computer aided diagnosis. During the research it was necessary to correct non-uniform image illumination caused by dermatoscope lightning. The correction algorithm based on dermatoscope light intensity estimation was used. The proposed segmentation method is based on histogram skin modeling. Preliminary test results are promising, for the analyzed melanoma images mean Jaccard index of 89.48% and mean sensitivity of 92.45% were obtained (when compared to expert assessment).

Nevus atypical pigment network distinction and irregular streaks detection in skin lesions images

There is no suitable golden standard for the detection of atypical pigment network and irregular streaks applied to skin lesion images. This information however is important in assessment of melanoma in skin dermatoscopic images. Thus there is a need for development of image analysis techniques that satisfy at least subjective criteria defined by dermatologists. In this paper we present the application of histogram based features for detection of atypical pigment network and shape based features supplemented by artificial neural network for detection of irregular streaks. Preliminary test results are promising, for analyzed melanoma images we get 97,7% correctly detected pigmentation networks and 94,8% correctly detected irregular streaks. This paper constitutes the part of our efforts to implement the ELM 7-point checklist in order to support melanoma diagnosis and to automate this process.

On the Influence of Image Features Wordlength Reduction on Texture Classification

Texture is present in a large number of medical images. Its structure codes selected properties of visualized organ and tissues so texture can be rich source of information regarding their condition. Quantitative texture analysis plays significant role in imaging diagnosis support systems, enabling segmentation of analyzed organs, detection of lesions, and assessment of the degree of their pathological change. Unfortunately, medical images are often corrupted by noise which affect texture based image features. One of the steps of texture feature extraction is reduction of gray levels number which is performed after a normalization of pixel intensities inside a region of interest. This reduces the noise effect on texture feature values. We demonstrated, based on analysis of natural and MR images, that such reduction improves classification accuracy while reducing the computational costs.

Lytic Region Recognition in Hip Radiograms by Means of Statistical Dominance Transform

Total hip replacement is the accepted treatment procedure of the end stage degeneration of the hip joint. Instability of the prosthesis might be recognized on the radiographic images as area of bone radio - lucency adjacent to the prosthesis pin. However, the very important issue of radiological recognition of periprosthetic lucent areas reflecting the lysis remains a challenge. Small dimensions and fuzzy borders of the lytic areas makes them difficult regions to recognize. Additional factors as high BMI of the patients and/or radiograms taken through a mattress can make the evaluation even more difficult, while small lucent areas might be additionally blurred and of very low contrast. The paper presents a new approach for quantitative recognition of preprothetic lytic areas. We have proposed a multistep algorithm utilizing Statistical Dominance Transform for detection of lytic areas on digital radiograms. Preliminary results are quite promising. It was demonstrated that location and shape of the detected lytic region is in good agreement with assessment by radiologists.

QMaZda — Software tools for image analysis and pattern recognition

Qmazda is a package of software tools for digital image analysis. They compute shape, color and texture attributes in arbitrary regions of interest, implement selected algorithms of discriminant analysis and machine learning, and enable texture based image segmentation. The algorithms generalize a concept of texture to three-dimensional data to enable analysis of volumetric images from magnetic resonance imaging or computed tomography scanners. The tools support a complete workflow — from image examples as an input to classification rules as an output. The extracted knowledge can be further used in custom made image analysis systems. Here we also present an application of QMaZda to identify defective barley kernels. The cereal seeds variability is high, therefore, characterization and discriminant analysis of such the biological objects is challenging and non-trivial. The software is available free of charge and open source, with executables for Windows, Linux and OS X platforms.

Preprocessing of barley grain images for defect identification

A malt is one of intermediate ingredients for a brewing industry. The quality of barley used for malting have essential impact on the final product flavor. An automatic system for a barley grains inspection, utilizing computer vision methods, can provide an objective quality assessment. We present image preprocessing steps of grain inspection system. Main preprocessing steps are: segmentation of grain kernel images, identification of dorsal and ventral sides of the kernels, aligning them with respect to the germ-brush direction. The results of preprocessing are presented and discussed.

Barley defects identification.

In brewing industry, quality of barley accepted for malt production is essential. The visual inspection of grain for malting is performed by a qualified expert. The process is time-consuming, expensive, and still may yield unreproducible results. Therefore, there is a need for automatic systems, based on computer vision, able to verify grain properties. We present a concept of such the system, which implements image preprocessing, texture, color and shape feature extraction, supervised learning and selected classification algorithms. The results of classification are presented and discussed.

Blue Whitish Veil, Atypical Vascular Pattern and Regression Structures Detection in Skin Lesions Images

There is no suitable standard for the detection of blue whitish veil atypical vascular pattern and regression structures applied to skin lesion images. This information however is important in assessment of melanoma in skin dermatoscopic images. Thus there is a need for development of image analysis techniques that satisfy at least subjective criteria required by dermatologists. In this paper the application of color based image features for detection of blue whitish veil and atypical vas-cular pattern is presented. Preliminary test results are promising; for analyzed melanoma images the accuracy of developed methods provides 78 % correctly detected blue whitish veils, 84 % correctly detected atypical vascular pattern, and 86,5 % correctly detected regression structures. This paper is a contribution to the computer aided diagnostic system implementing the ELM 7-point check-list aimed at melanoma detection.