Segmentation and surface reconstruction of a cadaver heart on Mimics software

The Visible Korean research team used Mimics software (Materialise, Leuven, Belgium) for the segmentation and subsequent surface reconstruction of heart structures using information obtained from sectioned images of a cadaver. Twenty-six heart components were outlined in advance on Photoshop (Adobe Systems, San Jose, CA, USA). By use of the Mimics, the outlined images were then browsed along with the vertical planes as well as the 3-dimensional surface models, which were immediately built by piling the images. Erroneous delineation was readily detected and revised until satisfactory heart models were acquired. The surface models and the selected sectioned images in horizontal, coronal, and sagittal planes were inputted into a PDF file, where any combinations of reconstructed constituents could be displayed and rotated by the user. Mimics software accelerated the segmentation and surface reconstruction of heart anatomical structures. Similar benefits hopefully result from various serial images of other organs. The PDF file, and plane and stereoscopic image data are being distributed to others, and should prove valuable for medical students and clinicians.

Surface models of the male urogenital organs built from the Visible Korean using popular software

Unlike volume models, surface models, which are empty three-dimensional images, have a small file size, so they can be displayed, rotated, and modified in real time. Thus, surface models of male urogenital organs can be effectively applied to an interactive computer simulation and contribute to the clinical practice of urologists. To create high-quality surface models, the urogenital organs and other neighboring structures were outlined in 464 sectioned images of the Visible Korean male using Adobe Photoshop; the outlines were interpolated on Discreet Combustion; then an almost automatic volume reconstruction followed by surface reconstruction was performed on 3D-DOCTOR. The surface models were refined and assembled in their proper positions on Maya, and a surface model was coated with actual surface texture acquired from the volume model of the structure on specially programmed software. In total, 95 surface models were prepared, particularly complete models of the urinary and genital tracts. These surface models will be distributed to encourage other investigators to develop various kinds of medical training simulations. Increasingly automated surface reconstruction technology using commercial software will enable other researchers to produce their own surface models more effectively

A Systematic Review of Three-Dimensional Printing in Liver Disease

The purpose of this review is to analyse current literature related to the clinical applications of 3D printed models in liver disease. A search of the literature was conducted to source studies from databases with the aim of determining the applications and feasibility of 3D printed models in liver disease. 3D printed model accuracy and costs associated with 3D printing, the ability to replicate anatomical structures and delineate important characteristics of hepatic tumours, and the potential for 3D printed liver models to guide surgical planning are analysed. Nineteen studies met the selection criteria for inclusion in the analysis. Seventeen of them were case reports and two were original studies. Quantitative assessment measuring the accuracy of 3D printed liver models was analysed in five studies with mean difference between 3D printed models and original source images ranging from 0.2 to 20%. Fifteen studies provided qualitative assessment with results showing the usefulness of 3D printed models when used as clinical tools in preoperative planning, simulation of surgical or interventional procedures, medical education, and training. The cost and time associated with 3D printed liver model production was reported in 11 studies, with costs ranging from US$13 to US$2000, duration of production up to 100 h. This systematic review shows that 3D printed liver models demonstrate hepatic anatomy and tumours with high accuracy. The models can assist with preoperative planning and may be used in the simulation of surgical procedures for the treatment of malignant hepatic tumours

Descripción de la anatomía seccional del gato en criosecciones axiales, resonancia magnética y tomografía computarizada

RESUMEN: Los modelos virtuales muestran la anatomía de forma realista a través de objetos tridimensionales que representan la estructura anatómica. La aplicación de un modelo virtual interactivo que describa la anatomía segmentaria de un felino facilitaría el entendimiento de su anatomía. Se usaron tres cadáveres donados de gatos machos adultos sin ninguna alteración de su anatomía. Uno de los cadáveres fue escaneado con un resonador, obteniendo 135 imágenes de RM a intervalos de 3 mm. Otro fue escaneado con un tomógrafo, obteniendo 330 imágenes de TC a intervalos de 2 mm. El último cadáver fue congelado a -20°C dentro de un bloque con fluido de embebido, seccionado y fotografiado de forma seriada, obteniendo 178 fotografías de cortes transversos a intervalos de 2,5 mm de todo el cuerpo del gato. Las imágenes de TC se procesaron, segmentaron y se crearon cuatro reconstrucciones 3D. Adicionalmente, se modelaron alrededor de 418 estructuras para crear un gato 3D y se diseñaron modelos de los sistemas óseo, muscular, circulatorio, nervioso, respiratorio, digestivo, urinario y tegumentario. Se desarrolló un software, un atlas virtual interactivo con el modelo 3D del gato y con la librería de imágenes. El atlas permite explorar de forma libre y fácil su contenido, de modo que las imágenes seccionales y estructuras del cuerpo del gato se pueden comprender fácilmente. Esperamos que las imágenes y el software producidos durante esta investigación, sean una herramienta de enseñanza útil en medicina veterinaria.ABSTRACT: The virtual models show the anatomy realistically through three-dimensional objects that represent the anatomical structure. The application of an interactive virtual model that describes the segmental anatomy of a feline would facilitate the understanding of anatomy and therefore the interpretation and use of diagnostic imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI). Three donated cadavers from adult male cats without any alteration of their anatomy were used. One of the corpses was scanned with a resonator obtaining 135 MR images at 3 mm intervals. Another was scanned with a tomograph obtaining 330 CT images at 2 mm intervals. The last cadaver was frozen at -20°C inside a block with embedded fluid and sectioned and photographed serially obtaining 178 photographs of transverse cuts at intervals of 2.5 mm of the entire body of the cat. The CT images were processed, segmented and four 3D reconstructions were created. Additionally, around 418 structures were modeled to create a 3D cat, and designed models of the skeletal, muscular, circulatory, nervous, respiratory, digestive, urinary and integumentary systems. A software was developed, an interactive virtual atlas with the 3D model of the cat and with the image library. The atlas allows to freely and easily explore your content so the sectional images and structures of the cat's body can be easily understood. We hope that the images and software produced during this research will be a useful teaching tool in veterinary Medicin

Automated assessment for early and late blight leaf diseases using extended segmentation and optimized features

Early and late blight diseases lead to substantial damage to vegetable crop productions and economic losses. As a modern solution, machine learning-based plant disease assessment aims to assess the disease incidence and severity through the disease region of interest (ROI) and its extracted features. In the case of existing conventional classifier methods, extracting the features involves generalized ROI segmentation that loosely follows the disease inference. As a result, accuracy is reduced, and the fuzzy boundary region that carries potential properties for improving feature characterization capability is truncated from the ROI. Besides, most of the existing practices extract only the global features, This leads to redundant and extensive feature vector, which causes increased complexity and underperformance. Furthermore, individual lesion severity is not considered in the assessment. This thesis addresses the issue of the ROI segmentation by using color thresholding based on ratios of leaf green color intensity to incorporate the fuzzy boundary region, denoted as extended ROI (EROI). Secondly, the issue of the feature extraction is addressed by the proposed localized feature extraction method to reduce complexity and improve disease classification performance. Based on the color and texture morphological properties of the individual lesions within the EROI, color coherence vector and local binary patterns features are extracted. As a result, a pathologically optimized feature vector is obtained, which is used to build a support vector machine classifier to classify between the disease types of early blight, late blight, and healthy leaves. lastly, a 2-tier assessment is proposed. The disease type classification is given as the first tier, while the leaf lesion area ratios of the individual lesions are given as severity quantification for the second tier. Overall, the proposed EROI segmentation method reduced under-segmentation by up to 80%. The proposed optimized feature reduced the execution run-time by up to 50% and achieved an average classification performance of up to 99%. Finally, the quantified severity is in close agreement with the ground truth by achieving an average accuracy of 93%

Technical Report on Semiautomatic Segmentation Using the Adobe Photoshop

The purpose of this research is to enable users to semiautomatically segment the anatomical structures in magnetic resonance images (MRIs), computerized tomographs (CTs), and other medical images on a personal computer. The segmented images are used for making 3D images, which are helpful to medical education and research. To achieve this purpose, the following trials were performed. The entire body of a volunteer was scanned to make 557 MRIs. On Adobe Photoshop, contours of 19 anatomical structures in the MRIs were semiautomatically drawn using MAGNETIC LASSO TOOL and manually corrected using either LASSO TOOL or DIRECT SELECTION TOOL to make 557 segmented images. In a similar manner, 13 anatomical structures in 8,590 anatomical images were segmented. Proper segmentation was verified by making 3D images from the segmented images. Semiautomatic segmentation using Adobe Photoshop is expected to be widely used for segmentation of anatomical structures in various medical images

Image texture analysis of transvaginal ultrasound in monitoring ovarian cancer

Ovarian cancer has the highest mortality rate of all gynaecologic cancers and is the fifth most common cancer in UK women. It has been dubbed “the silent killer” because of its non-specific symptoms. Amongst various imaging modalities, ultrasound is considered the main modality for ovarian cancer triage. Like other imaging modalities, the main issue is that the interpretation of the images is subjective and observer dependent. In order to overcome this problem, texture analysis was considered for this study. Advances in medical imaging, computer technology and image processing have collectively ramped up the interest of many researchers in texture analysis. While there have been a number of successful uses of texture analysis technique reported, to my knowledge, until recently it has yet to be applied to characterise an ovarian lesion from a B-mode image. The concept of applying texture analysis in the medical field would not replace the conventional method of interpreting images but is simply intended to aid clinicians in making their diagnoses. Five categories of textural features were considered in this study: grey-level co-occurrence matrix (GLCM), Run Length Matrix (RLM), gradient, auto-regressive (AR) and wavelet. Prior to the image classification, the robustness or how well a specific textural feature can tolerate variation arises from the image acquisition and texture extraction process was first evaluated. This includes random variation caused by the ultrasound system and the operator during image acquisition. Other factors include the influence of region of interest (ROI) size, ROI depth, scanner gain setting, and „calliper line‟. Evaluation of scanning reliability was carried out using a tissue-equivalent phantom as well as evaluations of a clinical environment. iii Additionally, the reliability of the ROI delineation procedure for clinical images was also evaluated. An image enhancement technique and semi-automatic segmentation tool were employed in order to improve the ROI delineation procedure. The results of the study indicated that two out of five textural features, GLCM and wavelet, were robust. Hence, these two features were then used for image classification purposes. To extract textural features from the clinical images, two ROI delineation approaches were introduced: (i) the textural features were extracted from the whole area of the tissue of interest, and (ii) the anechoic area within the normal and malignant tissues was excluded from features extraction. The results revealed that the second approach outperformed the first approach: there is a significant difference in the GLCM and wavelet features between the three groups: normal tissue, cysts, and malignant. Receiver operating characteristic (ROC) curve analysis was carried out to determine the discriminatory ability of textural features, which was found to be satisfactory. The principal conclusion was that GLCM and wavelet features can potentially be used as computer aided diagnosis (CAD) tools to help clinicians in the diagnosis of ovarian cancer

Documenting bloodstain patterns concealed beneath architectural paint layers using multi-spectral forensic photography techniques

xvii, 678 leaves : ill. (chiefly col.) ; 29 cmIncludes abstract and appendices.Includes bibliographical references (leaves 174-185).The development of forensic photography techniques can aid agencies in the documentation of information regarding crime scene cleanup. This study compared reflective infrared, reflective ultraviolet, and fluorescence photography in the documentation of bloodstain patterns that had been concealed beneath layers of architectural paint. High Dynamic Range (HDR) photography, as well as a chemical analysis of all four paint types using Raman, Fibre Optic Reflectance, and Attenuated Total Internal Reflectance-Fourier Transform Infrared Spectroscopy was performed. The photography results for reflective infrared were negative; reflective ultraviolet for two of the four paint types were positive. Fluorescence photography had the most definitive visual information for the two white paints but were concluded negative for black and maroon. HDR was concluded to be negative for reflective infrared and reflective ultraviolet; however, results for fluorescence were positive. Finally, spectroscopy results supported visual information as well as providing spectral data relevant for understanding specific chemical observations