Relative Foreground Area Error Research Articles

Segmentasi Daun Cendana Berbasis Citra Menggunakan Otsu Thresholding

The segmentation process is the separation of parts of the object area from the background in an image, so that segmented objects can be processed for other purposes such as pattern recognition. The results of segmentation must be accurate, if it is not accurate in separating objects in the image it will affect the results of further processing. The segmentation process is carried out using the Otsu Thresholding method on sandalwood leaf images by first applying the Median filter to reduce noise. After obtaining the segmented image, then performing performance measurements. The segmentation results from each test are evaluated using the RAE (relative foreground area error) and ME (misclassification error). The segmentation results of 8 sandalwood leaf images from 2 existing conditions show that, sandalwood leaf image segmentation with good leaf conditions obtains the best segmentation results with smaller errors of 5 image data. While the images of sandalwood leaves affected by the disease as many as 3 image data have more diverse areas so that the segmentation results are not good without any morphological process

Computer-Aided System of the Mandibular Cortical Bone Porosity Assessment on Digital Panoramic Radiographs.

The loss of bone mineral density (BMD) in various sites of the body, including the mandible, is the main sign of osteoporosis. Thus, the computer-aided diagnosis (CAD) system was developed for bone density assessment and patients were classified into normal, osteopenia, and osteoporosis groups using a digital panoramic radiograph. Data of dental panoramic radiographs and corresponding BMD assessments from 123 postmenopausal women were collected. For the proposed CAD system test, regions of interest (ROI) that were located below the left and right mental foramen on dental panoramic radiographs were determined. The width and texture of the mandibular cortical bone in each ROI were used to classify the data into normal, osteopenia, and osteoporosis classes. The width of the mandibular cortical was measured using the polynomial fitting method. The texture feature of the cortical bone is obtained by calculating the average value of the grayscale intensity of cortical bone. The classification result was obtained by using a multiclass support vector machine. The experimental results using 10-fold cross-validation showed that the proposed system achieved an average accuracy of 86.50% for osteoporosis classification on dental panoramic radiographs. The average misclassification error and relative foreground area error of the segmentation process were 5.21 and 12.98%, respectively. From the analysis of the cortical width measurement process, highest average mandibular cortical width (MCW) was found in the normal patient category compared with the other classes. This research showed that the proposed computer-aided system can be used for osteoporosis and osteopenia assessment by measuring the MCW and texture on dental panoramic radiographs with the average system accuracy of 89.52%.

Defect Detection Based on Segmentation of Thermographic Images of Frequency Modulated Thermal Wave Imaging

To improve the usefulness of the data, the raw images acquired during non-destructive testing should be processed by image processing techniques. In this paper, by Frequency Modulated Thermal Wave Imaging, we use the image fusion technique to boost the detection capability of defects in a GFRP sample with 25 squared Teflon inserts of different sizes positioned at various depths. In applications such as detection, image segmentation is useful where it is difficult to process the entire image at a time. In this paper, Adaptive Thresholding Image segmentation is used to classify the delamination in Thermographic Images of Infrared Non-Destructive Research on images captured at two different times. Image fusion is later applied to segmented images. Image fusion is used to merge two or more pictures of a different focus and to provide the best picture quality. Fusion is carried out using the Basic Averaging Method here. Using Relative Foreground Area error, the performance of the proposed method is quantitatively assessed. The region and shape of an object are important parameters in the case of Non-Destructive Evaluation. Such parameters are contrasted with current methods of segmentation.

SEPARATION OF OVERLAPPING OBJECT SEGMENTATION USING LEVEL SET WITH AUTOMATIC INITALIZATION ON DENTAL PANORAMIC RADIOGRAPH

To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%. Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology.

Automatic image slice marking propagation on segmentation of dental CBCT

Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly used to help doctors provide more detailed information for further xamination. Teeth segmentation on CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have related information, the semi-automatic image segmentation method, that needs manual marking from the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will be propagated as the marker for the segmentation of the next slices. The experimental results show that the proposed method is successful in segmenting the teeth on CBCT images with the value of Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively

Spatial Condition in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs

Dental panoramic radiographs heavily depend on the performance of the segmentation method due to the presence of unevenly illumination and low contrast of the images. Conditional Spatial Fuzzy C-mean (csFCM) Clustering have been proposed to achieve through the incorporation of the component and added in the FCM to cluster grouping. This algorithm directs with consideration conditioning variables that consider membership value. However, csFCM does not consider Intuitionistic Fuzzy Set to take final membership and final non-membership value into account, the effect does not wipe off the deviation by illumination and low contrast of the images completely for improvement to skip some scope. In this current paper, we introduced a new image segmentation method namely Conditional Spatial in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs. Our proposed method adds hesitation function aiming to settle the indication of the knowledge lack that belongs to the final membership function to get a better segmentation result. The experiment result shows this method achieves better segmentation performance with misclassification error (ME) and relative foreground area error (RAE) values are 4.77 and 4.27 respectively.

Interactive Image Segmentation using Neighborhood Spatial Information and Statistical Grey Level on Dental Panoramic Radiograph

In dental panoramic radiographs, grey-level intensity information has been widely used for object segmentation in digital image. However, low contrast in the radiograph image causes high ambiguity that can cause the inconsistency of classification result. Since the grey-level intensity of background and object is almost similar, so in order to improve the segmentation result, the spatial distance on neighborhod region is applied. In this paper, we proposed a novel strategy to measure the distance using neighborhod spatial information and statistical grey level technique for image segmentation. The proposed method starts by calculating adjacency matrix and measured spatial distance on neighborhood region. Since the value of both distances are not in the same range, then the normalization is needed. The distances merging is approached by tuning the weight using several constant values. The experiment results show that our proposed merging strategy has better segmentation result based on Relative Foreground Area Error value.

AUTOMATIC DETERMINATION OF SEEDS FOR RANDOM WALKER BY SEEDED WATERSHED TRANSFORM FOR TUNA IMAGE SEGMENTATION

Tuna fish image classification is an important part to sort out the type and quality of the tuna based upon the shape. The image of tuna should have good segmentation results before entering the classification stage. It has uneven lighting and complex texture resulting in inappropriate segmentation. This research proposed method of automatic determination seeded random walker in the watershed region for tuna image segmentation. Random walker is a noise-resistant segmentation method that requires two types of seeds defined by the user, the seed pixels for background and seed pixels for the object. We evaluated the proposed method on 30 images of tuna using relative foreground area error (RAE), misclassification error (ME), and modified Hausdroff distances (MHD) evaluation methods with values of 4.38%, 1.34% and 1.11%, respectively. This suggests that the seeded random walker method is more effective than exiting methods for tuna image segmentation.

Seeded Region Growing pada Ruang Warna HSI untuk Segmentasi Citra Ikan Tuna

The image of the tuna before entering process classification, it must have a good segmentation results. The result of good segmentation is object and background separate clearly. The image of tuna which has a distribution of light that is uneven and has a complex texture will produce an error segmentation. One method of image segmentation was seeded region growing and parameters that used only two, namely seed and threshold. This research proposed method seeded region growing in the HSI color space for image segmentation of tuna. The Color space of RGB (red green blue) on image of tuna transformed into a color space HSI (hue saturation intensity) then only the hue color space used as segmentation by using seeded region growing. Determination of seed and threshold parameters can do manually and the result of the segmentation do refinement with mathematical morphology. The experiment using 30 image of tuna to segmentation and evaluation methods using RAE (relative foreground area error), MAE (missclassification error) and the MHD (modified Hausdroff distance). The image of the tuna successfully performed segmentation evidenced by a value RAE, ME and MHD respectively are 5,40%, 1,53% dan 0,41%.

Object-based image segmentation and retrieval for texture images

In this paper, object retrieval techniques based on texture segmentation are proposed for use in local region texture images. In order to perform the segmentation of a local region texture image in this study, three texture features were first extracted: coarseness, contrast and directionality. For image retrieval, texture features for visual perception (TFVP), colour histogram for K-mean (CHKM) and shape features for principal moments of inertia (SFPMI) features of the object were extracted after segmentation. TFVP, CHKM and SFPMI are the texture, colour features and shape features, respectively, of the object region in an image; they are used for image retrieval. In order to more precisely represent the accuracy of this proposed method, the image segmentation/image retrieval results are compared with other methods. It was shown that the segmentation performance of the proposed method evaluated using misclassification error (ME), relative foreground area error (RAE), modified Hausdorff distance (MHD) and AVE is better than other methods. The results clearly show that the performances of the proposed method for local region texture image retrieval are significantly superior to those of the other methods for global region texture image retrieval. The object segmentation in this study can accurately segment the image object, thereby obtaining image retrieval results with much higher precision.

Automatic Segmentation of Phalanx and Epiphyseal/Metaphyseal Region by Gamma Parameter Enhancement Algorithm

The performance of bone age assessment is highly correlated with the extraction of bony tissue from soft tissues, and the key problem is how to successfully separate epiphyseal/metaphyseal region of interests (EMROIs) from the background and soft tissue. In our experiment, a series of image preprocessing procedures are used to exclude the background and locate the EMROIs of left-hand radiographs. Subsequently, automatic gamma parameter enhancement is applied to test the two segmentation methods (adaptive two-means clustering algorithm and gradient vector flow snake) among children of different age (the age from 2 to 16 years for 80 girls and boys). Four error measurements of misclassification error, relative foreground area error, modified Hausdorff distances, and edge mismatch, are included to evaluate the segmentation performance. The result shows that the two segmentation algorithms are corresponding to different ranges of optimal gamma parameters. Furthermore, the margin of EMROIs can be obtained more precisely by developing an automatic bone age assessment method with the gamma parameter enhancement. Keywords: Bone age, Epiphyseal/metaphyseal, Segmentation, Gamma parameter enhancement, Adaptive two-means clustering

Evaluation of single and multi-thresholdentropy-based algorithms for folded substrate analysis

This paper presents a detailed evaluation of two variants of Maximum Entropy image segmentation algorithm (single and multi-thresholding) with respect to their performance on segmenting test images showing folded substrates. The segmentation quality was determined by evaluating values of four different measures: misclassification error, modified Hausdorff distance, relative foreground area error and positive-negative false detection ratio. New normalization methods were proposed in order to combine all parameters into a unique algorithm evaluation rating. The segmentation algorithms were tested on images obtained by three different digitalisation methods covering four different surface textures. In addition, the methods were also tested on three images presenting a perfect fold. The obtained results showed that Multi-Maximum Entropy algorithm is better suited for the analysis of images showing folded substrates.

An improved GVF snake based breast region extrapolation scheme for digital mammograms

To accurately extrapolate the breast region from a mammogram is a crucial stage of breast mass analysis. It significantly influences the overall analysis accuracy and processing speed of the whole breast mass analysis. In this paper, a novel edge map adjusting gradient vector flow snake (EMA GVF snake) algorithm for extrapolation of breast region from mammograms is proposed. In the proposed algorithm, the median filter is used to filter out the noise in a mammogram, the scale down stage is used to resize down the mammogram size (hence speeding up the extrapolation). The binarization processing stage and the morphological erosion processing stage are used to find a rough breast border. Then a novel gradient adjusting stage is applied to get a modified edge map and the gradient vector flow snake (GVF snake) is used to get the accurate breast border from the rough breast border. The proposed algorithm is tested on 322 digital mammograms from the Mammogram Image Analysis Society database. The mean error function, misclassification error function and the relative foreground area error function are conducted to evaluate the results of the detected breast border and the extracted breast region. Experimental results show that the breast border extrapolated by the proposed algorithm approximately follows the breast border extrapolated by an expert radiologist. Experimental results also show that the proposed algorithm is more robust and precise than the traditional GVF snake scheme for the breast extrapolation on mammograms.

Fast and fully automatic phalanx segmentation using a grayscale-histogram morphology algorithm

Bone age assessment is a common radiological examination used in pediatrics to diagnose the discrepancy between the skeletal and chronological age of a child; therefore, it is beneficial to develop a computer-based bone age assessment to help junior pediatricians estimate bone age easily. Unfortunately, the phalanx on radiograms is not easily separated from the background and soft tissue. Therefore, we proposed a new method, called the grayscale-histogram morphology algorithm, to segment the phalanges fast and precisely. The algorithm includes three parts: a tri-stage sieve algorithm used to eliminate the background of hand radiograms, a centroid-edge dual scanning algorithm to frame the phalanx region, and finally a segmentation algorithm based on disk traverse-subtraction filter to segment the phalanx. Moreover, two more segmentation methods: adaptive two-mean and adaptive two-mean clustering were performed, and their results were compared with the segmentation algorithm based on disk traverse-subtraction filter using five indices comprising misclassification error, relative foreground area error, modified Hausdorff distances, edge mismatch, and region nonuniformity. In addition, the CPU time of the three segmentation methods was discussed. The result showed that our method had a better performance than the other two methods. Furthermore, satisfactory segmentation results were obtained with a low standard error.

Local Adaptive Thresholding of Pecan X-Ray Images: Reverse Water Flow Method

Segmentation of food product images by global thresholding methods has met with limited success due to unimodal image histograms and inhomogeneous backgrounds. Local adaptive thresholding methods have been successful in segmenting images with inhomogeneous backgrounds; however, there are limited studies applying them to food products. This article evaluates selected local adaptive thresholding methods and proposes a new faster method. The proposed method consists of modifications to the Oh water flow method. For pecan images, the proposed method and the Oh method were able to segment insect-eaten nutmeat and insect exit paths with orientation parallel to the x-ray beam. By adjusting the threshold, the proposed method was also able to segment insect exit paths with perpendicular orientation. The proposed method required only 38.9% of the computation time required by the Oh method. Two applications of the single Otsu threshold method and the Kim method both worked well for larger defects. Pixel misclassification error and relative foreground area error were used as objective indices to evaluate the segmentation results, and the results by the Oh method and the proposed method were comparable. The proposed method also performed well for sample citrus, metallic structure, and cell images. The proposed method is faster and simpler in approach, yet robust and accurate. Features can be extracted from the segmented images for defect classification, thus providing a step toward on-line non-destructive machine vision inspection. The proposed method should be extendable to images with unimodal histograms and inhomogeneous backgrounds.

A protozoan parasite extraction scheme for digital microscopic images

Pathogenic protozoan parasites can cause human to get many diseases, such as, amoebiasis, typhoid fever and cholera, etc. Different protozoan parasites vary greatly in their structural and biochemical properties. Digital images are extensively applied to medical fields for doctors and pathologists to analyze pathological sections and further diagnose diseases. The aim of this paper is to develop protozoan parasite extraction techniques to segment protozoan parasites from microscopic images. The proposed scheme has precise segmentation ability even if the image is with poor quality or complex background. Experimental results show that the proposed scheme can gain 96.64% average correct rate, and about 0.04, 0.45 and 0.06 of the average error rates: misclassification error (ME), region non-uniformity (RN) and relative foreground area error (RFAE), respectively.