Segmentasi Variasi Pencahayaan Citra Tomat Menggunakan Marker Controlled Watershed Dan Arimoto Entropy Untuk Perbaikan Citra

. Tomatoes image acquisition in outdoors condition results in an image that cannot be processed because of lighting variation on the glossy surface. Lighting variation is one of the problems in image processing because the resulting color values on tomatoes is lost from the affected area due to lighting variation. This research is meant to improve the image of tomatoes with lighting variations in the preprocessing stage. Segmentation methods proposed to detect and eliminate lighting variation is marker-controlled watershed with Arimoto entropy. After eliminating the detected area with lighting, tomatoes image are improved in three ways, namely by applying RGB average, searching the value of pixels with pixels index, and using a moving window with various kernel sizes. The error segmentation of the proposed method is by 36.67%, which better than the previous method. The best results tomato image enhancement is by using a moving window with a kernel size 15x15

Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS

Being able to effectively identify clouds and monitor their evolution is one important step toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation technique is developed using tools from image processing techniques. This method integrates morphological image gradient magnitudes to separable cloud systems and patches boundaries. A varying scale-kernel is implemented to reduce the sensitivity of image segmentation to noise and capture objects with various finenesses of the edges in remote-sensing images. The proposed method is flexible and extendable from single- to multi-spectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellites (GOES-R) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Cloud Classification System) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potential to improve rain detection and estimation skills with an average of more than 45% gain comparing to the segmentation technique used in PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to 98%

Segmentasi Citra Ikan Tuna Menggunakan Gradient-Barrier Watershed Berbasis Analisis Hierarki Klaster dan Regional Credibility Merging

Abstract. The main issue of object identification in tuna image is the difficulty of extracting the entire contour of tuna physical features, because it is often influenced by uneven illumination and the ambiguity of object edges in tuna image. We propose a novel segmentation method to optimize the determination of tuna region using GBW-AHK and RCM. GBW-AHK is used to optimize the determination of adaptive threshold in order to reduce over-segmented watershed regions. Then, RCM merges the remaining regions based on two merging criteria, thus it produces two main areas of segmentation, the object extraction of tuna and the background. The experimental results on 25 tuna images demonstrate that the proposed method successfully produced an image segmentation with the average value of RAE by 4.77%, ME of 0.63%, MHD of 0.20, and the execution time was 11.61 seconds. Keywords: watershed, gradient-barrier, hierarchical cluster analysis, regional credibility merging, tuna segmentation Abstrak. Kendala utama identifikasi objek tuna pada citra ikan tuna adalah sulitnya mengekstraksi seluruh kontur tubuh ikan, karena seringkali dipengaruhi faktor iluminasi yang tidak merata dan ambiguitas tepi objek pada citra. Penelitian ini mengusulkan metode segmentasi baru yang mengoptimalkan penentuan region objek tuna menggunakan Gradient-Barrier Watershed berbasis Analisis Hierarki Klaster (GBW-AHK) dan Regional Credibility Merging (RCM). Metode GBW-AHK digunakan untuk mengoptimalkan penentuan adaptif threshold untuk mereduksi region watershed yang over-segmentasi. Kemudian RCM melakukan penggabungan region sisa hasil reduksi berdasarkan dua syarat penggabungan hingga dihasilkan dua wilayah utama segmentasi, yakni ekstraksi objek ikan tuna dan background. Hasil eksperimen pada 25 citra ikan tuna membuktikan bahwa metode usulan berhasil melakukan segmentasi dengan nilai rata-rata relative foreground area error (RAE) 4,77%, misclassification error (ME) 0,63%, modified Hausdorff distance (MHD) 0,20, dan waktu eksekusi 11,61 detik. Kata Kunci: watershed, gradient-barrier, analisis hierarki klaster, regional credibility merging, segmentasi tun

New methods for automatic quantification of microstructural features using digital image processing

Thermal and mechanical processes alter the microstructure of materials, which determines their mechanical properties. This makes reliable microstructural analysis important to the design and manufacture of components. However, the analysis of complex microstructures, such as Ti6Al4V, is difficult and typically requires expert materials scientists to manually identify and measure microstructural features. This process is often slow, labour intensive and suffers from poor repeatability. This paper overcomes these challenges by proposing a new set of automated techniques for 2D microstructural analysis. Digital image processing algorithms are developed to isolate individual microstructural features, such as grains and alpha lath colonies. A segmentation of the image is produced, where regions represent grains and colonies, from which morphological features such as; grain size, volume fraction of globular alpha grains and alpha colony size can be measured. The proposed measurement techniques are shown to obtain similar results to existing manual methods while drastically improving speed and repeatability. The benefits of the proposed approach when measuring complex microstructures are demonstrated by comparing it with existing analysis software. Using a few parameter changes, the proposed techniques are effective on a variety of microstructure types and both SEM and optical microscopy image

Herramienta de segmentación semiautomática de imágenes mediante visión por computador

El proyecto propone una herramienta de segmentación semiautomàtica de imágenes usando visión por computador. El objectivo es poder segmentar imágenes que contienen gestos realizados con las manos para su posterior interpretación como parte de una aplicación posterior. Para llevar a cabo esta herramienta primeramente se realiza un estudio del estado del arte para poder analitzar las capacidades de las herramientas actuales que ya realizan esta función, tratando de aprovechar lo que éstas nos oferecen. Posteriormente al estudio se explica el desarrollo de la nueva herramienta, que consta de tres fases: preprocesamiento de la imagen, generación semiautomática de marcadores mediante el metodo connected labeled components, y la utilización del algoritmo Watershed que finalmente se encarga de hacer la segmentación final. Los resultados preliminares mostrados en este documento indican un gran potencial de la técnica desarrollada, ofreciendo resultados prometedores de segmentación sin requerir del usuario final más que marcar un punto en la imagen.This project proposes a semi-automatic computer vision tool for segmenting images. The final objective is to segment images which contains persons performing a series of gestures; our methodology aims at segmenting both hands and the head as part of a bigger pipeline which would include the recognition of the actions performed. To develop our tool we have first carried out a research on the State-of-the-art on semi-automatic segmentation in order to explore the capabilities of existint approaches in order to obtain knowledge to create our tool. After this, we propose our segmentation methodology which consists of three stages: image preprocessing, semi-automatic foreground marker generation using connected labeled components and finally, the use of Watersheds to obtain the final segmentation. Preliminary results show the potential of our tool to be used for segmenting the proposed structures; our method is able to obtain promising segmentation results with minimal user interaction.El projecte proposa una eina de segmentació semiautomàtica d'imatges utilitzant visió per computador. L'objectiu es pode segmentar imatges que contenen gestos realitzats amb les mans per a la seva posterior interpretació com a parte d'una aplicació posterior. Per portar a terme aquesta eina primerament es realitza un estudi del estat de l'art per a poder analitzar les capacitats de les eines actuals que ja realitzen aquesta funció, tactant d'aprofitar el que aquestes ofereixen. Posteriorment a l'estudi s'explica el desenvolupament de la nova eina, que consta de tres fase: preprocesament de l'imatge, generació semiautomàtica de marcadors mitjançant el mètode connected labeled components, i l'utilització de l'algoritme Watershed que finalment s'encarrega de fer la segmentació final. Els resultats preliminars mostrats en aquest document indiquen un gran potencial de la tècnica desenvolupada, oferint resultats prometedors de segmentació sense requerir de l'usuari final més que per a marcar un punt en la imatge

Metody automatycznej segmentacji tekstur na obrazach RTG płuc

Making a medical diagnosis using X-ray images in short called RTG is still the most popular method, especially in the case of lung diseases. Doctors and radiologists analyze such photos and according to their expertise try to make a diagnosis or direct for additional tests. The main research goal is to develop a method to isolate pathological changes from digital chest X-ray images. It combines the actions of extraction of nodular changes and fibrous in the lungs, which is to allow the introduction of a new tool supporting the diagnostic process. The proposed method is to maximize the possibility of getting the biggest amount of information that will contribute to a more efficient diagnosis by a person making the diagnosis. The X-ray image processing process will consist of stages containing methods converting the initial image. These include actions on the histogram of the studied images, where based on existing adaptation and histogram alignment properties, a new property has been developed which improves the quality of the analyzed images. Due to possible occurrence of noise filtration and morphological operations were used to reduce that issue. In such processed image it is possible to segment pathological changes on a given part of the image. An additional stage is applying the classification on a fragment or whole of the X-ray image being examined. Person conducting the diagnosis of the photo by selecting given element of the lung receives feedback what is the marked lung element - possible nodular change, fibrosis, lung field, bones. The research was carried out under the supervision of a radiologist. The tests were performed on real chest X-ray images without patients' personal data obtained through cooperation with a radiologist expert. The proposed methods in the expert's evaluation improve the diagnostic process, whereas the presented method complements the imaging process using X-ray images and supports the work on describing and making a diagnosis

A marker-based watershed method for X-ray image segmentation

Digital X-ray images are the most frequent modality for both screening and diagnosis in hospitals. To facilitate subsequent analysis such as quantification and computer aided diagnosis (CAD), it is desirable to exclude image background. A marker-based watershed segmentation method was proposed to segment background of X-ray images. The method consisted of six modules: image preprocessing, gradient computation, marker extraction, watershed segmentation from markers, region merging and background extraction. One hundred clinical direct radiograph X-ray images were used to validate the method. Manual thresholding and multiscale gradient based watershed method were implemented for comparison. The proposed method yielded a dice coefficient of 0.964 ± 0.069, which was better than that of the manual thresholding (0.937 ± 0.119) and that of multiscale gradient based watershed method (0.942 ± 0.098). Special means were adopted to decrease the computational cost, including getting rid of few pixels with highest grayscale via percentile, calculation of gradient magnitude through simple operations, decreasing the number of markers by appropriate thresholding, and merging regions based on simple grayscale statistics. As a result, the processing time was at most 6 s even for a 3072 × 3072 image on a Pentium 4 PC with 2.4 GHz CPU (4 cores) and 2G RAM, which was more than one time faster than that of the multiscale gradient based watershed method. The proposed method could be a potential tool for diagnosis and quantification of X-ray images