Iterative thresholding for segmentation of cells from noisy images

Masked image modeling-based boundary reconstruction for 3D medical image segmentation

Accurate segmentation of 3D medical images is vital for computer-aided diagnosis. However, the complexity of target morphological variations and a scarcity of labeled data make segmentation more challenging. Furthermore, existing models make it difficult to fully and efficiently integrate global and local information, which hinders structured knowledge acquisition. To overcome these challenges, we introduce the TNT Masking Network (TNT-MNet), a groundbreaking transformer-based 3D model that utilizes a transformer-in-transformer (TNT) encoder. For the first time, we present masked image modeling (MIM) in supervised learning, utilizing target boundary regions as masked prediction targets to enhance structured knowledge acquisition. We execute multiscale random masking on inner and outer tokens in online branch to tackle the challenge of segmenting organs and lesion regions with varying structures at multiple scales and to enhance modeling capabilities. In contrast, the target branch utilizes all tokens to guide the online branch to reconstruct the masked tokens. Our experiments suggest that TNT-MNet's performance is comparable, or even better, than state-of-the-art models in three medical image datasets (BTCV, LiTS2017, and BraTS2020) and effectively reduces the dependence on labeled data. The code and models are publicly available at https://github.com/changliu-work/TNT_MNet.

Neuromorphic applications in medicine

In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.

A neutrosophic-entropy based clustering algorithm (NEBCA) with HSV color system: A special application in segmentation of Parkinson's disease (PD) MR images

BACKGROUND AND OBJECTIVES

Brain MR images consist of three major regions: gray matter, white matter and cerebrospinal fluid. Medical experts make decisions on different serious diseases by evaluating the developments in these areas. One of the significant approaches used in analyzing the MR images were segmenting the regions. However, their segmentation suffers from two major problems as: (a) the boundaries of their gray matter and white matter regions are ambiguous in nature, and (b) their regions are formed with unclear inhomogeneous gray structures. For these reasons, diagnosis of critical diseases is often very difficult.

METHODS

This study presented a new method for MR image segmentation, which consisted of two main parts as: (a) neutrosophic-entropy based clustering algorithm (NEBCA), and (b) HSV color system. The NEBCA's role in this study was to perform segmentation of MR regions, while HSV color system was used to provide better visual representation of features in segmented regions.

RESULTS

Application of the proposed method was demonstrated in 30 different MR images of Parkinson's disease (PD). Experimental results were presented individually for the NEBCA and HSV color system. The performance of the proposed method was evaluated in terms of statistical metrics used in an image segmentation domain. Experimental results, including statistical analysis reflected the efficiency of the proposed method over the existing well-known image segmentation methods available in literature. For the proposed method and existing methods, the average CPU time (in nanosecond) was computed and it was found that the proposed method consumed less time to segment MR images.

CONCLUSION

The proposed method can effectively segment different regions of MR images and can very clearly represent those segmented regions.

A neutrosophic-entropy based adaptive thresholding segmentation algorithm: A special application in MR images of Parkinson's disease

Brain MR images are composed of three main regions such as gray matter, white matter and cerebrospinal fluid. Radiologists and medical practitioners make decisions through evaluating the developments in these regions. Study of these MR images suffers from two major issues such as: (a) the boundaries of their gray matter and white matter regions are ambiguous and unclear in nature, and (b) their regions are formed with unclear inhomogeneous gray structures. These two issues make the diagnosis of critical diseases very complex. To solve these issues, this study presented a method of image segmentation based on the neutrosophic set (NS) theory and neutrosophic entropy information (NEI). By nature, the proposed method is adaptive to select the threshold value and is entitled as neutrosophic-entropy based adaptive thresholding segmentation algorithm (NEATSA). In this study, experimental results were provided through the segmentation of Parkinson's disease (PD) MR images. Experimental results, including statistical analyses showed that NEATSA can segment the main regions of MR images very clearly compared to the well-known methods of image segmentation available in literature of pattern recognition and computer vision domains.

Polarization image segmentation of radiofrequency ablated porcine myocardial tissue

Optical polarimetry has previously imaged the spatial extent of a typical radiofrequency ablated (RFA) lesion in myocardial tissue, exhibiting significantly lower total depolarization at the necrotic core compared to healthy tissue, and intermediate values at the RFA rim region. Here, total depolarization in ablated myocardium was used to segment the total depolarization image into three (core, rim and healthy) zones. A local fuzzy thresholding algorithm was used for this multi-region segmentation, and then compared with a ground truth segmentation obtained from manual demarcation of RFA core and rim regions on the histopathology image. Quantitative comparison of the algorithm segmentation results was performed with evaluation metrics such as dice similarity coefficient (DSC = 0.78 ± 0.02 and 0.80 ± 0.02), sensitivity (S_n = 0.83 ± 0.10 and 0.91 ± 0.08), specificity (S_p = 0.76 ± 0.17 and 0.72 ± 0.17) and accuracy (Acc = 0.81 ± 0.09 and 0.71 ± 0.10) for RFA core and rim regions, respectively. This automatic segmentation of parametric depolarization images suggests a novel application of optical polarimetry, namely its use in objective RFA image quantification.

A computational approach to detect and segment cytoplasm in muscle fiber images

We developed a computational approach to detect and segment cytoplasm in microscopic images of skeletal muscle fibers. The computational approach provides computer-aided analysis of cytoplasm objects in muscle fiber images to facilitate biomedical research. Cytoplasm in muscle fibers plays an important role in maintaining the functioning and health of muscular tissues. Therefore, cytoplasm is often used as a marker in broad applications of musculoskeletal research, including our search on treatment of muscular disorders such as Duchenne muscular dystrophy, a disease that has no available treatment. However, it is often challenging to analyze cytoplasm and quantify it given the large number of images typically generated in experiments and the large number of muscle fibers contained in each image. Manual analysis is not only time consuming but also prone to human errors. In this work we developed a computational approach to detect and segment the longitudinal sections of cytoplasm based on a modified graph cuts technique and iterative splitting method to extract cytoplasm objects from the background. First, cytoplasm objects are extracted from the background using the modified graph cuts technique which is designed to optimize an energy function. Second, an iterative splitting method is designed to separate the touching or adjacent cytoplasm objects from the results of graph cuts. We tested the computational approach on real data from in vitro experiments and found that it can achieve satisfactory performance in terms of precision and recall rates.

A method for automatic segmentation of nuclei in phase-contrast images based on intensity, convexity and texture

This paper presents a method for automatic segmentation of nuclei in phase-contrast images using the intensity, convexity and texture of the nuclei. The proposed method consists of three main stages: preprocessing, h-maxima transformation-based marker controlled watershed segmentation ( h-TMC), and texture analysis. In the preprocessing stage, a top-hat filter is used to increase the contrast and suppress the non-uniform illumination, shading, and other imaging artifacts in the input image. The nuclei segmentation stage consists of a distance transformation, h-maxima transformation and watershed segmentation. These transformations utilize the intensity information and the convexity property of the nucleus for the purpose of detecting a single marker in every nucleus; these markers are then used in the h-TMC watershed algorithm to obtain segments of the nuclei. However, dust particles, imaging artifacts, or prolonged cell cytoplasm may falsely be segmented as nuclei at this stage, and thus may lead to an inaccurate analysis of the cell image. In order to identify and remove these non-nuclei segments, in the third stage a texture analysis is performed, that uses six of the Haralick measures along with the AdaBoost algorithm. The novelty of the proposed method is that it introduces a systematic framework that utilizes intensity, convexity, and texture information to achieve a high accuracy for automatic segmentation of nuclei in the phase-contrast images. Extensive experiments are performed demonstrating the superior performance ( precision = 0.948; recall = 0.924; F1-measure = 0.936; validation based on  ∼ 4850 manually-labeled nuclei) of the proposed method.

Non-parametric and integrated framework for segmenting and counting neuroblastic cells within neuroblastoma tumor images

Neuroblastoma is a malignant tumor and a cancer in childhood that derives from the neural crest. The number of neuroblastic cells within the tumor provides significant prognostic information for pathologists. An enormous number of neuroblastic cells makes the process of counting tedious and error-prone. We propose a user interaction-independent framework that segments cellular regions, splits the overlapping cells and counts the total number of single neuroblastic cells. Our novel segmentation algorithm regards an image as a feature space constructed by joint spatial-intensity features of color pixels. It clusters the pixels within the feature space using mean-shift and then partitions the image into multiple tiles. We propose a novel color analysis approach to select the tiles with similar intensity to the cellular regions. The selected tiles contain a mixture of single and overlapping cells. We therefore also propose a cell counting method to analyse morphology of the cells and discriminate between overlapping and single cells. Ultimately, we apply watershed to split overlapping cells. The results have been evaluated by a pathologist. Our segmentation algorithm was compared against adaptive thresholding. Our cell counting algorithm was compared with two state of the art algorithms. The overall cell counting accuracy of the system is 87.65 %.

An evolutionary tabu search for cell image segmentation

Many engineering problems can be formulated as optimization problems. It has become more and more important to develop an efficient global optimization technique for solving these problems. In this paper, we propose an evolutionary tabu search (ETS) for cell image segmentation. The advantages of genetic algorithms (GA) and TS algorithms are incorporated into the proposed method. More precisely, we incorporate "the survival of the fittest" from evolutionary algorithms into TS. The method has been applied to the segmentation of several kinds of cell images. The experimental results show that the new algorithm is a practical and effective one for global optimization; it can yield good, near-optimal solutions and has better convergence and robustness than other global optimization approaches.

Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

The segmentation of leukocytes and their components acts as the foundation for all automated image-based hematological disease recognition systems. Perfection in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Since the diagnostic information content of the segmented images is plentiful, suitable segmentation routines need to be developed for better disease recognition. Clustering is an essential image segmentation procedure which segments an image into desired regions. A judicious integration of rough sets and fuzzy sets is suitably employed towards leukocyte segmentation in a clustering framework. In this study, the goodness of fuzzy sets and rough sets is suitably integrated to achieve improved segmentation performance. The membership concept of fuzzy sets endow is efficient handling of overlapping partitions, and the rough sets provide a reasonable solution to deal with uncertainty, vagueness, and incompleteness in data. Such synergistic combination gives the proposed scheme an edge over standard cluster-based segmentation techniques, that is, K-means, K-medoid, fuzzy c-means, and rough c-means. Comparative analysis reveals that the hybrid rough fuzzy c-means algorithm is robust in segmenting stained blood microscopic images. The accomplished segmented nucleus and cytoplasm of a leukocyte can be used for feature extraction which leads to automated leukemia detection.

Three-dimensional segmentation of nuclei and mitotic chromosomes for the study of cell divisions in live Drosophila embryos

Drosophila embryogenesis is an established model to investigate mechanisms and genes related to cell divisions in an intact multicellular organism. Progression through the cell cycle phases can be monitored in vivo using fluorescently labeled fusion proteins and time-lapse microscopy. To measure cellular properties in microscopic images, accurate and fast image segmentation methods are a critical prerequisite. To quantify static and dynamic features of interphase nuclei and mitotic chromosomes, we developed a three-dimensional (3D) segmentation method based on multiple level sets. We tested our method on 3D time-series images of live embryos expressing histone-2Av-green fluorescence protein. Our method is robust to low signal-to-noise ratios inherent to high-speed imaging, fluorescent signals in the cytoplasm, and dynamic changes of shape and texture. Comparisons with manual ground-truth segmentations showed that our method achieves more than 90% accuracy on the object as well as voxel levels and performs consistently throughout all cell cycle phases and developmental stages from syncytial blastoderm to postblastoderm mitotic domains.

Phenotypic profiling of the human genome reveals gene products involved in plasma membrane targeting of SRC kinases

SRC proteins are non-receptor tyrosine kinases that play key roles in regulating signal transduction by a diverse set of cell surface receptors. They contain N-terminal SH4 domains that are modified by fatty acylation and are functioning as membrane anchors. Acylated SH4 domains are both necessary and sufficient to mediate specific targeting of SRC kinases to the inner leaflet of plasma membranes. Intracellular transport of SRC kinases to the plasma membrane depends on microdomains into which SRC kinases partition upon palmitoylation. In the present study, we established a live-cell imaging screening system to identify gene products involved in plasma membrane targeting of SRC kinases. Based on siRNA arrays and a human model cell line expressing two kinds of SH4 reporter molecules, we conducted a genome-wide analysis of SH4-dependent protein targeting using an automated microscopy platform. We identified and validated 54 gene products whose down-regulation causes intracellular retention of SH4 reporter molecules. To detect and quantify this phenotype, we developed a software-based image analysis tool. Among the identified gene products, we found factors involved in lipid metabolism, intracellular transport, and cellular signaling processes. Furthermore, we identified proteins that are either associated with SRC kinases or are related to various known functions of SRC kinases such as other kinases and phosphatases potentially involved in SRC-mediated signal transduction. Finally, we identified gene products whose function is less defined or entirely unknown. Our findings provide a major resource for future studies unraveling the molecular mechanisms that underlie proper targeting of SRC kinases to the inner leaflet of plasma membranes.

Towards high performance cell segmentation in multispectral fine needle aspiration cytology of thyroid lesions

Thyroid nodule is a common cancer of the thyroid gland that affects up to 20% of the world population and approximately 50% of 60-year-old persons. Early detection and screening of the disease, especially analysis by fine needle aspiration cytology (FNAC), has led to improved diagnosis and management of the disease. Simultaneously, advances in imaging technology has enabled the rapid digitization of large volumes of FNAC specimen leading to increased interest in computer assisted diagnosis (CAD). This has led to development of a variety of algorithms for automated analysis of FNAC images, but due to the large scale memory and computing resource requirements, has had limited success in clinical use. In this paper, we present our experiences with two parallel versions of a code used for texture-based segmentation of thyroid FNAC images, a critical first step in realizing a fully automated CAD solution. An MPI version of the code is developed to exploit distributed memory compute resources such as PC clusters. An OpenMP version is developed for the currently emerging multi-core CPU architectures, which allow for parallel execution on every desktop system. Experiments are performed with image sizes ranging from 1024 x 1024 pixels up to 12288 x 12288 pixels with 21 spectral channels. Both versions are evaluated for performance and scalability.

Three-dimensional surface texture visualization of bone tissue through epifluorescence-based serial block face imaging

Serial block face imaging is a microscopy technique in which the top of a specimen is cut or ground away and a mosaic of images is collected of the newly revealed cross-section. Images collected from each slice are then digitally stacked to achieve 3D images. The development of fully automated image acquisition devices has made serial block face imaging more attractive by greatly reducing labour requirements. The technique is particularly attractive for studies of biological activity within cancellous bone as it has the capability of achieving direct, automated measures of biological and morphological traits and their associations with one another. When used with fluorescence microscopy, serial block face imaging has the potential to achieve 3D images of tissue as well as fluorescent markers of biological activity. Epifluorescence-based serial block face imaging presents a number of unique challenges for visualizing bone specimens due to noise generated by sub-surface signal and local variations in tissue autofluorescence. Here we present techniques for processing serial block face images of trabecular bone using a combination of non-uniform illumination correction, precise tiling of the mosaic in each cross-section, cross-section alignment for vertical stacking, removal of sub-surface signal and segmentation. The resulting techniques allow examination of bone surface texture that will enable 3D quantitative measures of biological processes in cancellous bone biopsies.

Notch sensitivity of PEEK in monotonic tension

Poly(ether-ether-ketone) (PEEK) has been used as a load bearing orthopaedic implant material with clinical success. All of the orthopaedic applications contain stress concentrations (notches) in their design; however, little work has been done to examine the stress-strain behavior of PEEK in the presence of a notch. This work examines both the stress-strain behavior and the fracture behavior of neat PEEK in a uniaxial loaded condition, and in circumferentially grooved round bar specimens with different elastic stress concentration factors. It was found that the material shows ductile necking in the smooth condition and that this is almost completely suppressed in the notched conditions. Additionally, the deformation and fracture micromechanisms changed drastically, from one of plastic deformation and void coalescence to one dominated by crazing and brittle fast fracture. This change in mechanism was explained via Neuber's theory of stresses at a notch.

Live cell tracking based on cellular state recognition from microscopic images

The analysis of cell motion is an essential process in fundamental medical studies because most active cellular functions involve motion. In this paper, a computer-assisted motion analysis system is proposed for cell tracking. In the proposed tracking process, unlike in conventional tracking methods, cellular states referring to the cellular life cycle are defined and appropriate strategies are adopted for cells at different states. The use of cellular state recognition allows detection of possible cell division and hence can improve the robustness of cell tracking. Experimental results show that cells can be successfully segmented and tracked over a long period of time, and the proposed system is found to be as accurate as manual tracking. Various quantitative analyses and visualizations are used to represent cell motion, which demonstrates the usefulness of the proposed system in the study of cell dynamics.

Automated three-dimensional detection and counting of neuron somata

We present a novel approach for automated detection of neuron somata. A three-step processing pipeline is described on the example of confocal image stacks of NeuN-stained neurons from rat somato-sensory cortex. It results in a set of position landmarks, representing the midpoints of all neuron somata. In the first step, foreground and background pixels are identified, resulting in a binary image. It is based on local thresholding and compensates for imaging and staining artifacts. Once this pre-processing guarantees a standard image quality, clusters of touching neurons are separated in the second step, using a marker-based watershed approach. A model-based algorithm completes the pipeline. It assumes a dominant neuron population with Gaussian distributed volumes within one microscopic field of view. Remaining larger objects are hence split or treated as a second neuron type. A variation of the processing pipeline is presented, showing that our method can also be used for co-localization of neurons in multi-channel images. As an example, we process 2-channel stacks of NeuN-stained somata, labeling all neurons, counterstained with GAD67, labeling GABAergic interneurons, using an adapted pre-processing step for the second channel. The automatically generated landmark sets are compared to manually placed counterparts. A comparison yields that the deviation in landmark position is negligible and that the difference between the numbers of manually and automatically counted neurons is less than 4%. In consequence, this novel approach for neuron counting is a reliable and objective alternative to manual detection.

Segmentation of touching cell nuclei using gradient flow tracking

Reliable cell nuclei segmentation is an important yet unresolved problem in biological imaging studies. This paper presents a novel computerized method for robust cell nuclei segmentation based on gradient flow tracking. This method is composed of three key steps: (1) generate a diffused gradient vector flow field; (2) perform a gradient flow tracking procedure to attract points to the basin of a sink; and (3) separate the image into small regions, each containing one nucleus and nearby peripheral background, and perform local adaptive thresholding in each small region to extract the cell nucleus from the background. To show the generality of the proposed method, we report the validation and experimental results using microscopic image data sets from three research labs, with both over-segmentation and under-segmentation rates below 3%. In particular, this method is able to segment closely juxtaposed or clustered cell nuclei, with high sensitivity and specificity in different situations.

An automated method for cell detection in zebrafish

Quantification of cells is a critical step towards the assessment of cell fate in neurological disease or developmental models. Here, we present a novel cell detection method for the automatic quantification of zebrafish neuronal cells, including primary motor neurons, Rohon-Beard neurons, and retinal cells. Our method consists of four steps. First, a diffused gradient vector field is produced. Subsequently, the orientations and magnitude information of diffused gradients are accumulated, and a response image is computed. In the third step, we perform non-maximum suppression on the response image and identify the detection candidates. In the fourth and final step the detected objects are grouped into clusters based on their color information. Using five different datasets depicting zebrafish cells, we show that our method consistently displays high sensitivity and specificity of over 95%. Our results demonstrate the general applicability of this method to different data samples, including nuclear staining, immunohistochemistry, and cell death detection.

Segmentation of Brain Immunohistochemistry Images Using Clustering of Linear Centroids and Regional Shapes

A generalized clustering algorithm utilizing the geometrical shapes of clusters for segmentation of colored brain immunohistological images is presented. To simplify the computation, the dimension of vectors composed from the pixel RGB components is reduced from three to two by applying a de-correlation mapping with the orthogonal bases of the eigenvectors of the auto-covariance matrix. Since the brain immunohistochemical images have stretched clusters that appear long and narrow in geometrical shape, we use centroids of straight lines instead of single points to approximate the clusters. An iterative algorithm is developed to optimize the linear centroids by minimizing the approximation mean-squared error. The partitioning of the two-dimensional vector domain into three portions classifies each image pixel into one of the three classes: The microglial cell cytoplasm, the combined hematoxylin stained cell nuclei and the neuropil, and the pale background. Regions of the combined hematoxylin stained cell nuclei and the neuropil are to be separated based on the differences in their regional shapes. The segmentation results of real immunohistochemical images of brain microglia are provided and discussed.

Efficient segmentation framework of cell images in noise environments

In this paper, we propose an efficient segmentation method that exploits local information for automated cell segmentation. This method introduces a new criterion function based on statistical structure of the objects in cell image. Each pixel is initially assigned to the most probable region and then the pixel assignment process is iteratively updated by a new criterion function until steady state is reached. We apply the proposed method to cervical cell images as well as the corresponding noisy images that are contaminated by Gaussian noise. The performance of the proposed method is evaluated based on the results from both normal and noisy cell images.

High-throughput analysis of multispectral images of breast cancer tissue

Statistical analysis of genetic changes within cell nuclei that are far from the primary tumor would help determine whether such changes have occurred prior to tumor invasion. To determine whether the gene amplification in cells is morphologically and/or genetically related to the primary tumor requires quantitative evaluation of a large number of cell nuclei from continuous meaningful structures such as milk-ducts, tumors, etc., located relatively far from the primary tumor. To address this issue, we have designed an integrated image analysis software system for high-throughput segmentation of nuclei. Filters such as Beltrami flow-based reaction-diffusion, directional diffusion, etc., were used to pre-process the images resulting in a better segmentation. The accurate shape of the segmented nucleus was recovered using an iterative "shrink-wrap" operation. The study of two cases of ductal carcinoma in situ in breast tissue supports the biological observation regarding the existence of a preferential intraductal invasion, and therefore a common origin, between the primary tumor and the gene amplification in the cell-nuclei lining the ductal structures in the breast.

Quantitative image analysis in mammary gland biology

In this paper we present a summary of recent quantitative approaches used for the analysis of macro and microscopic images in mammary gland biology. The advantages and disadvantages of whole mount analysis, reconstruction of serial tissue sections and nucleus/cell segmentation of either conventional and confocal images are discussed, as are applications of quantitative image analysis, such as quantification of protein levels or vasculature measurements in normal tissue and cancer. Integration of quantitative imaging into the further study of the mammary gland holds the promise of better understanding its tissue complexity that evolves during development, differentiation and disease.

Applications of image analysis to anatomic pathology: realities and promises

Image Analysis in Pathology is viewed as an ancillary method meant to provide objective support in the resolution of difficult problems. Its Achilles heel is the process of nuclear segmentation (delimitation of the nuclear membrane) which is extremely difficult in pathology materials. Although interactive segmentation procedures are available no reliable fully automatic method has been described. The only application of image analysis that has truly succeeded in Pathology is DNA ploidy measurement. A very desirable application is the quantitation of immunohistochemical markers, which is technically challenging, has been resolved only in certain cases and is unlikely to have a general solution. Nuclear quantitation has repeatedly proven to be helpful in reaching differential diagnoses, in particular when based on size distributions of nuclear profiles rather than its average, but is hampered by the segmentation problem discussed above. Texture analysis of chromatin is an exciting, mathematically complex application likely to succeed, for which many approaches have been described. Finally a diagnosis (classification) can be obtained based on algorithms applied to multiple descriptors of tumor cells (for instance nuclear sizes, chromatin texture, shape, etc). The best classificatory approaches are neural networks (a form of artificial intelligencee), multivariate analysis, and logistic regression (statistical).

Harmonic cut and regularized centroid transform for localization of subcellular structures

Two novel computational techniques, harmonic cut and regularized centroid transform, are developed for segmentation of cells and their corresponding substructures observed with an epi-fluorescence microscope. Harmonic cut detects small regions that correspond to small subcellular structures. These regions also affect the accuracy of the overall segmentation. They are detected, removed, and interpolated to ensure continuity within each region. We show that interpolation within each region (subcellular compartment) is equivalent to solving the Laplace equation on a multi-connected domain with irregular boundaries. The second technique, referred to as the regularized centroid transform, aims to separate touching compartments. This is achieved by adopting a quadratic model for the shape of the object and relaxing it for final segmentation.

Interactive 3-dimensional segmentation of MRI data in personal computer environment

We describe a method of interactive three-dimensional segmentation and visualization for anatomical magnetic resonance imaging (MRI) data in a personal computer environment. The visual feedback necessary during 3-D segmentation was provided by a ray casting algorithm, which was designed to allow users to interactively decide the visualization quality depending on the task-requirement. Structures such as gray matter, white matter, and facial skin from T1-weighted high-resolution MRI data were segmented and later visualized with surface rendering. Personal computers with central processing unit (CPU) speeds of 266, 400, and 700 MHz, were used for the implementation. The 3-D visualization upon each execution of the segmentation operation was achieved in the order of 2 s with a 700 MHz CPU. Our results suggest that 3-D volume segmentation with semi real-time visual feedback could be effectively implemented in a PC environment without the need for dedicated graphics processing hardware.

Cell image segmentation with kernel-based dynamic clustering and an ellipsoidal cell shape model

In this paper, we propose a novel approach to cell image segmentation under severe noise conditions by combining kernel-based dynamic clustering and a genetic algorithm. Our method incorporates a priori knowledge about cell shape. That is, an elliptical cell contour model is introduced to describe the boundary of the cell. Our method consists of the following components: (1) obtain the gradient image; (2) use the gradient image to obtain points which possibly belong to cell boundaries; (3) adjust the parameters of the elliptical cell boundary model to match the cell contour using a genetic algorithm. The method is tested on images of noisy human thyroid and small intestine cells.