An edge relocation segmentation algorithm

An automated procedure that refines the nuclear contour of a previously segmented nucleus is described. The algorithm makes use of intensity information, edge magnitude information and both object and edge connectivity information. This automated procedure generates a closed contour precisely along the edge of the nucleus. The procedure was tested on a database of 3,680 red-green-blue images of thionin-SO2 and orange II-stained cervical cells obtained from normal and dysplastic samples. When used in conjunction with a simple threshold selection algorithm and an artifact removal routine, this edge relocation algorithm resulted in the correct segmentation of over 98% of the nuclei. Only 63 (1.7%) of all nuclei were incorrectly segmented.     

High resolution segmentation of cervical cells.

A major problem in the automation of cervical cytology screening is the segmentation of cell images. This paper presents the present status of the work on that problem at the University of Uppsala. A dual resolution system is used. Suspect malignant cells are located at 4 mu resolution. Each such cell is rescanned at 0.5 mu resolution at two different wavelengths, 530 and 570 nm. The nucleus and the cytoplasm are isolated each by two independent methods. For the nucleus adaptive thresholding in the histogram of the 570 nm image and a contouring in a radially transformed version of that image is used. For the cytoplasm a two dimensional thresholding in the 2D histogram and a contouring in a radially transformed version of the 530 nm image is used. If the two nuclear masks agree the surrounding area is checked for disturbing objects. If also the cytoplasm masks agree and are without disturbing objects the whole cell is accepted. The result of the cytoplasm masks agree and are without disturbing objects the whole cell is accepted. The result of the segmentation is thus three categories; free cells, free nuclei and rejected objects. The shape of the objects belonging to the former two categories is checked and irregularly shaped ones are rejected as probably consisting of several overlapping nuclei. Cells passing also this test are classified as normal or malignant. The experience from using this algorithm is discussed and areas for further research are pointed out.     

A comparison of some quick and simple threshold selection methods for stained cells

The segmentation accuracy of four fast and simple gray-scale threshold selection methods were compared using a data base of stained cervical cell images. Some postprocessing was applied to the segmented images to increase the accuracy of the nuclear segmentation. The most accurate method correctly segmented the cytoplasm of 81% of the cell images and the nuclei of 78% of the cell images in the data base.     

A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing

Evaluation of blood smear is a commonly clinical test these days. Most of the time, the hematologists are interested on white blood cells (WBCs) only. Digital image processing techniques can help them in their analysis and diagnosis. For example, disease like acute leukemia is detected based on the amount and condition of the WBC. The main objective of this paper is to segment the WBC to its two dominant elements: nucleus and cytoplasm. The segmentation is conducted using a proposed segmentation framework that consists of an integration of several digital image processing algorithms. Twenty microscopic blood images were tested, and the proposed framework managed to obtain 92% accuracy for nucleus segmentation and 78% for cytoplasm segmentation. The results indicate that the proposed framework is able to extract the nucleus and cytoplasm region in a WBC image sample.     

Automated image detection and segmentation in blood smears.

A simple technique which automatically detects and then segments nucleated cells in Wright's giemsa-stained blood smears is presented. Our method differs from others in 1) the simplicity of our algorithms; 2) inclusion of touching (as well as nontouching) cells; and 3) use of these algorithms to segment as well as to detect nucleated cells employing conventionally prepared smears. Our method involves: 1) acquisition of spectral images; 2) preprocessing the acquired images; 3) detection of single and touching cells in the scene; 4) segmentation of the cells into nuclear and cytoplasmic regions; and 5) postprocessing of the segmented regions. The first two steps of this algorithm are employed to obtain high-quality images, to remove random noise, and to correct aberration and shading effects. Spectral information of the image is used in step 3 to segment the nucleated cells from the rest of the scene. Using the initial cell masks, nucleated cells which are just touching are detected and separated. Simple features are then extracted and conditions applied such that single nucleated cells are finally selected. In step 4, the intensity variations of the cells are then used to segment the nucleus from the cytoplasm. The success rate in segmenting the nucleated cells is between 81 and 93%. The major errors in segmentation of the nucleus and the cytoplasm in the recognized nucleated cells are 3.5% and 2.2%, respectively.     

Segmentation of color images from serous cytology for automated cell classification

OBJECTIVE: To design an automated system for the classification of cells based on analysis of serous cytology, with the aim of segmenting both cytoplasm and nucleus using color information from the images as the main characteristic of the cells. STUDY DESIGN: The segmentation strategy uses color information coupled with mathematical morphology tools, such as watersheds. Cytoplasm and nuclei of all diagnostic cells are retained; erythrocytes and debris are eliminated. Special techniques are used for the separation of clustered cells. RESULTS: A large set of cells was assessed by experts to score the segmentation success rate. All cells were segmented whatever their spatial configurations. The average success rate was 92.5% for nuclei and 91.1% for cytoplasm. CONCLUSION: This color information-based segmentation of images of serous cells is accurate and provides a useful tool. This segmentation strategy will improve the automated classification of cells.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

A thresholding method for automatic cell image segmentation.

An algorithm for automatic segmentation of PAP-stained cell images and its digital implementation is described. First, the image is filtered in order to eliminate the granularily and small objects in the image which may upset the segmentation procedure. In a second step, information on gradient and compactness is extracted from the filtered image and stored in three histograms as functions of the extinction. From these histograms, two extinction thresholds are computed. These thresholds are suitable to separate the nucleus from the cytoplasm, and the cytoplasm from the background in the filtered image. Masks are determined in this way, and finally used to analyse the nucleus and the cytoplasm in the original image.     

Immunocytochemical and biochemical demonstration of formaldhyde dehydrogenase (class III alcohol dehydrogenase) in the nucleus.

Alcohol dehydrogenase (ADH), the major enzyme catalyzing the biological oxidation of ethanol in mammals, includes four classes with very different capacities for ethanol oxidation. Class III ADH is present in all the tissues and is well conserved throughout evolution. This enzyme has a low activity with ethanol, is specific for the glutathione-dependent oxidation of formaldehyde, and is therefore a formaldehyde dehydrogenase (FALDH). Until now there have been few and conflicting studies concerning its intracellular distribution, which is important for the understanding of its role in cell function. In the present work we used biochemical and immunocytochemical methods to assess the distribution of FALDH in rat hepatocytes and astroglial cells. With the glutathione-dependent formaldehyde dehydrogenase assay, we found the highest activity in the cytosol of hepatocytes and brain cells (12 and 2.6 mU/mg protein, respectively), but nuclei also exhibited significant activity (1.16 and 2.1 mU/mg protein, respectively). The immunocytochemical results showed the presence of FALDH binding sites in both the cytoplasm and the nucleus of the different cell types studied. Whereas no specific gold particle labeling was seen associated with any cytoplasmic component, in the nucleus the particles were found mainly over condensed chromatin and interchromatin regions. Finally, the gold particle density over both the nucleus and cytoplasm was greater in differentiated than in proliferating astrocytes in primary culture. In contrast, class I ADH, primarily responsible for ethanol metabolism, was found only in the cytoplasm of hepatocytes. We propose that one of the functions of FALDH is to protect cell structures, including DNA, from the toxic effects of endogenous formaldehyde, which is an intermediate in many metabolic process.     

Localisation of human papillomavirus 16 E7 oncoprotein changes with cell confluence

E7 is one of the best studied proteins of human papillomavirus type 16, largely because of its oncogenic potential linked to cervical cancer. Yet the sub-cellular location of E7 remains confounding, even though it has been shown to be able to shuttle between the nucleus and the cytoplasm. Here we show with immunocytochemistry that E7 proteins are located in the nucleus and cytoplasm in sub-confluent cells, but becomes cytoplasmic in confluent cells. The change in E7's location is independent of time in culture, cell division, cell cycle phase or cellular differentiation. Levels of E7 are also increased in confluent cells as determined by Western blotting. Our investigations have also uncovered how different analytical techniques influence the observation of where E7 is localised, highlighting the importance of technical choice in such analysis. Understanding the localisation of E7 will help us to better comprehend the function of E7 on its target proteins.     

Proteins in nucleocytoplasmic interactions. 3. Redistributions of nuclear proteins during and following mitosis in Amoeba proteus

The behavior of nuclear proteins in Amoeba proteus was studied by tritiated amino acid labeling, nuclear transplantation, and cytoplasmic amputation. During prophase at least 77% (but probably over 95%) of the nuclear proteins is released to the cytoplasm. These same proteins return to the nucleus within the first 3 hr of interphase. When cytoplasm is amputated from an ameba in mitosis (shen the nuclear proteins are in the cytoplasm), the resultant daughter nuclei are depleted in the labeled nuclear proteins. The degree of depletion is less than proportional to the amount of cytoplasm removed because a portion of rapidly migrating protein (a nuclear protein that is normally shuttling between nucleus and cytoplasm and is thus also present in the cytoplasm) which would normally remain in the cytoplasm is taken up by the reconstituting daughter nuclei. Cytoplasmic fragments cut from mitotic cells are enriched in both major classes of nuclear proteins, i.e. rapidly migrating protein and slow turn-over protein. An interphase nucleus implanted into such an enucleated cell acquires from the cytoplasm essentially all of the excess nuclear proteins of both classes. The data indicate that there is a lack of binding sites in the cytoplasm for the rapidly migrating nuclear protein. The quantitative aspects of the distribution of rapidly migrating protein between the nucleus and the cytoplasm indicate that the distribution is governed primarily by factors within the nucleus.     

Identification of the small nuclear RNAs associated with the mitotic chromosomes of Amoeba proteus

Amebas contain 7 electrophoretically distinct species of small nuclear RNAs (snRNAs), some of which are known to associate in a striking manner with mitotic chromosomes. These RNAs can be divided into 2 classes, one consisting of 4 snRNA species that shuttle in a non-random way between nucleus and cytoplasm during interphase and one consisting of 3 snRNA species that do not leave the nucleus at all during interphase. In the work reported here we sought to determine which class is associated with mitotic chromosomes. Through a series of micromanipulative procedures we arranged for the shuttling snRNAs to be the only radioactive molecules in the cell. Such cells were allowed to enter mitosis, whereupon they were fixed and subjected to autoradiography. In those cells no radioactive snRNAs were found associated with mitotic chromosomes. It is concluded, therefore, that those snRNAs that do associate with mitotic chromosomes must be one or more of the non-shuttling species. — In the Discussion, how the non-shuttling snRNAs may function in cell activities is considered.     

Evidence for discontinuous replication of circular mitochondrial DNA molecules from Novikoff rat ascites hepatoma cells

Double-forked circular molecules of mitochondrial DNA (mtDNA) from rat tissues, indicated by their form and size to be replicative intermediates, are of two structurally distinct classes. Molecules of the first class are totally double stranded. Molecules of the second class are defined by one daughter segment being totally or partially single stranded. Length histograms of daughter segments measuring between 2% and 44% of the total 5-µm molecular contour were constructed from samples of both classes of replicating molecules derived from mtDNA or Novikoff rat ascites hepatoma cells. For single strand-containing molecules, the lengths fell into eight distinct, reproducible groups with mean values separated by 4.1–7.6% of the circular contour length. For totally double stranded molecules, the lengths fell into seven groups, corresponding to seven of the groups found for single strand-containing molecules. These results suggest that along at least 44% of the contour of mtDNA molecules there exist discrete points at which DNA synthesis tends to be arrested. This may indicate that there are pauses in normal mtDNA synthesis. However, as the DNA used in these experiments was isolated from mitochondrial fractions, the findings may indicate that continuation of synthesis beyond specific points on the nucleotide strands requires a factor which is not available after cell disruption.     

THE CYTONUCLEOPROTEINS OF AMEBAE : I. Some Chemical Properties and Intracellular Distribution

Autoradiographs of whole Amoeba proteus host cells fixed after the implantation of single nuclei from A. proteus donors labeled with any one of 8 different radioactive amino acids showed that the label had become highly concentrated in the host cell nucleus as well as in the donor nucleus and that the cytoplasmic activity was relatively low. When these amebae were sectioned, the radioactivity was found to be homogeneously distributed throughout the nuclei. The effect of unlabeled amino acid "chaser," the solubility of the labeled material, and the long-term behavior of the labeled material gave evidence that the radioactivity was in protein. At equilibrium, the host cell nucleus contained approximately 30 per cent of the radioactivity distributed between the two nuclei. This unequal nuclear distribution is attributed to the presence of two classes of nuclear proteins: a non-migratory one that does not leave the nucleus during interphase, and a migratory one, called cytonucleoprotein, that shuttles between nucleus and cytoplasm in a non-random manner. It is estimated that between 12 per cent and 44 per cent of the cytonucleoproteins are present in the cytoplasm of a binucleate cell at any one moment. Nuclei of Chaos chaos host cells also concentrated label acquired from implanted radioactive A. proteus nuclei.     

Analysis of machine-selected cells with an image analysis system in normal and abnormal cervical specimens

An analysis has been performed of visual diagnostic criteria used in cervical cytology applied to machine selected cells in relation to automated classification based on variables, which can be recorded in an image system with automated cell search and segmentation, feature extraction and classification. A 98% accuracy could be obtained with the choice of the most ideal statistical methods for discrimination and the use of the most powerful variables recorded in the image system when compared with consensus of the visual diagnoses based on established cytological criteria for diagnosis of cancer and precancer of the cervix uteri. The most powerful discriminatory variables in the image system (of 17 recorded) for discrimination between normal and abnormal epithelial cells were, in addition to nuclear extinction, cytoplasmic extinction and cytoplasmic shape. It is concluded that the visual classification of cervical cells is highly accurate with experienced observers and that imaging microscopes can be trained to nearly equal this accuracy with appropriate statistical methods of discrimination. The problem of creating fully automated systems, however, also requires the inclusion of even more effective discriminatory variables and also the solution of such problems as automatic cell search, segmentation, artifact rejection, feature extraction, classification and electronic stability in order to become cost-effective.     

Automatic cell segmentation in cyto- and histometry using dominant contour feature points.

Automatic cell segmentation has various application potentials in cytometry and histometry. In this paper, an automatic cluster (touching) cell segmentation approach using the dominant contour feature points has been presented. Dominant feature points are the locations of indentation on the contour of the cluster. First, dominant feature points on the contour of the cluster are detected by distance profile. Next, using shape features of the cells, these feature points are selected for segmentation. We compared the results of the proposed method with manual segmentation and observed that the method has an overall accuracy about to 82%.     

PROTEINS IN NUCLEOCYTOPLASMIC INTERACTIONS : II. Turnover and Changes in Nuclear Protein Distribution with Time and Growth

In previous studies, we showed that essentially all the proteins of the Amoeba proteus nucleus could be classified either as Rapidly Migrating Proteins (RMP), which shuttle between nucleus and cytoplasm continuously at a relatively rapid rate during interphase, or as Slow Turnover Proteins (STP), which seem to move hardly at all during interphase. In this paper, we report on the kinetics and direction of the movement of both classes of protein, as well as on aspects of their localization, with and without growth. The effects of growth were observed with and without cell division. These nuclear proteins have been studied in several ways: by transplantation of labeled nuclei into unlabeled cells and noting the rate of distribution to cytoplasm and host cell nuclei; by repeated amputation of cytoplasm from labeled cells—with and without initially labeled cytoplasm—each amputation being followed by refeeding on unlabeled food; by noting the redistribution of the various protein classes following growth and cell division. The data show (a) labeled RMP equilibrate between a grafted labeled nucleus and an unlabeled host nucleus in ca. 3 hr, but are detectable in the latter less than 30 min after the operation; (b) STP label does, indeed, leave the nucleus and does so at a rate of ca. 25% of the nuclear total per cell generation (ca. 36–40 hr at 23°C); (c) the cytoplasm appears to have a reserve of material that is converted to RMP; (d) when labeled cells are amputated just before they would have divided and are refed unlabeled food after each amputation, there is a loss of 20–25% of the nuclear protein label with each amputation; (e) under the latter circumstances, an essentially complete turnover of all nuclear protein can be demonstrated.     

A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy

High-intensity focused ultrasound (HIFU) therapy has been used to treat uterine fibroids widely and successfully. Uterine fibroid segmentation plays an important role in positioning the target region for HIFU therapy. Presently, it is completed by physicians manually, reducing the efficiency of therapy. Thus, computer-aided segmentation of uterine fibroids benefits the improvement of therapy efficiency. Recently, most computer-aided ultrasound segmentation methods have been based on the framework of contour evolution, such as snakes and level sets. These methods can achieve good performance, although they need an initial contour that influences segmentation results. It is difficult to obtain the initial contour automatically; thus, the initial contour is always obtained manually in many segmentation methods. A split-and-merge-based uterine fibroid segmentation method, which needs no initial contour to ensure less manual intervention, is proposed in this paper. The method first splits the image into many small homogeneous regions called superpixels. A new feature representation method based on texture histogram is employed to characterize each superpixel. Next, the superpixels are merged according to their similarities, which are measured by integrating their Quadratic-Chi texture histogram distances with their space adjacency. Multi-way Ncut is used as the merging criterion, and an adaptive scheme is incorporated to decrease manual intervention further. The method is implemented using Matlab on a personal computer (PC) platform with Intel Pentium Dual-Core CPU E5700. The method is validated on forty-two ultrasound images acquired from HIFU therapy. The average running time is 9.54 s. Statistical results showed that SI reaches a value as high as 87.58%, and normHD is 5.18% on average. It has been demonstrated that the proposed method is appropriate for segmentation of uterine fibroids in HIFU pre-treatment imaging and planning.