Image Segmentation Evaluation Research Articles

Performance Evaluation of K-Mean and Fuzzy C-Mean Image Segmentation Based Clustering Classifier

This paper presents Evaluation K-mean and Fuzzy c-mean image segmentation based Clustering classifier. It was followed by thresholding and level set segmentation stages to provide accurate region segment. The proposed stay can get the benefits of the K-means clustering. The performance and evaluation of the given image segmentation approach were evaluated by comparing K-mean and Fuzzy c-mean algorithms in case of accuracy, processing time, Clustering classifier, and Features and accurate performance results. The database consists of 40 images executed by K-mean and Fuzzy c-mean image segmentation based Clustering classifier. The experimental results confirm the effectiveness of the proposed Fuzzy c-mean image segmentation based Clustering classifier. The statistical significance Measures of mean values of Peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE) and discrepancy are used for Performance Evaluation of K-mean and Fuzzy c-mean image segmentation. The algorithm’s higher accuracy can be found by the increasing number of classified clusters and with Fuzzy c-mean image segmentation.

Optimal segmentation of a high-resolution remote-sensing image guided by area and boundary

Image segmentation is the premise of object-based image analysis (OBIA), and obtaining an optimal segmentation result has been a desire for many researchers. This article proposes an optimal segmentation method for a high-resolution remote-sensing image that is guided by spatial features of area and boundary. This method achieves an optimal result through stepwise refinement on multi-scale segmentations. First, boundary strength is integrated into the choice for the optimal scale based on an improved unsupervised evaluation. Then, under-segmented objects (USOs) and over-segmented objects (OSOs) at the selected optimal scale are identified using a heterogeneity histogram and a slider-like threshold with the guidance of area and boundary. Finally, the corresponding objects, in a specific finer segmentation, are taken to replace the USOs at the optimal scale, and then the USOs and OSOs are refined by an effective merging mechanism. A heterogeneity-change-based merging criterion considering boundary, shape, spectral, and texture features is constructed for the merging of neighbouring objects. The proposed method is more effective than the unsupervised image segmentation evaluation and refinement (UISER) method as it uses spatial features to guide optimal choice of scale, and USO and OSO identification and refinement. Comparative experiments show that the spatial features used in the proposed method are effective for achieving an enhanced segmentation result.

Dissimilarity criteria and their comparison for quantitative evaluation of image segmentation: application to human retina vessels

The quantitative evaluation of image segmentation is an important and difficult task that is required for making a decision on the choice of a segmentation method and for the optimal tuning of its parameter values. To perform this quantitative evaluation, dissimilarity criteria are relevant with respect to the human visual perception, contrary to metrics that have been shown to be visually not adapted. This article proposes to compare eleven dissimilarity criteria together. The field of retina vessels image segmentation is taken as an application issue to emphasize the comparison of five specific image segmentation methods, with regard to their degrees of consistency and discriminancy. The DRIVE and STARE databases of retina images are employed and the manual/visual segmentations are used as a reference and as a control method. The so-called $$\epsilon $$ criterion gives results in agreement with perceptually based criterions for achieving the quantitative comparison.

A Learning-Based Framework for Supervised and Unsupervised Image Segmentation Evaluation

Image segmentation is a fundamental task in automatic image analysis. However, there is still no generally accepted effectiveness measure which is suitable for evaluating the segmentation quality in every application. In this paper, we propose an evaluation framework which benefits from multiple stand-alone measures. To this end, different segmentation evaluation measures are chosen to evaluate segmentation separately, and the results are effectively combined using machine learning methods. We train and implement this framework in our brand-new segmentation dataset which contains images of different contents with segmentation ground truth and Weizmann segmentation database (WSD). In addition, we provide human evaluation of image segmentation pairs to benchmark the evaluation results of the measures. Experimental results show a better performance than the stand-alone methods.

Evaluation of Image Segmentation and Filtering With Ann in the Papaya Leaf

Precision agriculture is area with lack of cheap technology. The refinement of the production system brings large advantages to the producer and the use of images makes the monitoring a more cheap methodology. Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In this paper is analyzed the method based on computational intelligence to work with image segmentation in the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for image segmentation and filtering, several variations of parameters and insertion impulsive noise were evaluated too. Satisfactory results are achieved with artificial neural for segmentation same with high noise levels.

Near sets through fuzzy similarity relation

Near sets are considered as an extension of rough sets. They are disjoint sets that resemble each other. In this paper, we proposed a class of near sets formed through a fuzzy binary relation and its partition. The application of near fuzzy set in the field of image segmentation evaluation is also mentioned.

Evaluation of data fusion and image segmentation in earth observation based rapid mapping workflows

This paper is an exploratory study, which aimed to discover the synergies of data fusion and image segmentation in the context of EO-based rapid mapping workflows. Our approach pillared on the geographic object-based image analysis (GEOBIA) focusing on multiscale, internally-displaced persons’ (IDP) camp information extraction from very high spatial resolution (VHSR) images. We applied twelve pansharpening algorithms to two subsets of a GeoEye-1 image scene that was taken over a former war-induced ephemeral settlement in Sri Lanka. A multidimensional assessment was employed to benchmark pansharpening algorithms with respect to their spectral and spatial fidelity. The multiresolution segmentation (MRS) algorithm of the eCognition Developer software served as the key algorithm in the segmentation process. The first study site was used for comparing segmentation results produced from the twelve fused products at a series of scale, shape, and compactness settings of the MRS algorithm. The segmentation quality and optimum parameter settings of the MRS algorithm were estimated by using empirical discrepancy measures. Non-parametric statistical tests were used to compare the quality of image object candidates, which were derived from the twelve pansharpened products. A wall-to-wall classification was performed based on a support vector machine (SVM) classifier to classify image objects candidates of the fused images. The second site simulated a more realistic crisis information extraction scenario where the domain expertise is crucial in segmentation and classification. We compared segmentation and classification results of the original images (non-fused) and twelve fused images to understand the efficacy of data fusion. We have shown that the GEOBIA has the ability to create meaningful image objects during the segmentation process by compensating the fused image’s spectral distortions with the high-frequency information content that has been injected during fusion. Our findings further questioned the necessity of the data fusion step in rapid mapping context. Bypassing time-intensive data fusion helps to actuate EO-based rapid mapping workflows. We, however, emphasize the fact that data fusion is not limited to VHSR image data but expands over many different combinations of multi-date, multi-sensor EO-data. Thus, further research is needed to understand the synergies of data fusion and image segmentation with respect to multi-date, multi-sensor fusion scenarios and extrapolate our findings to other remote sensing application domains beyond EO-based crisis information retrieval.

A Probabilistic Measure for Quantitative Evaluation of Image Segmentation

In this letter, we propose a probabilistic measure to evaluate the machine segmentation with multiple ground truths. The measure is designed for adaptively evaluating the structural information extracted from the segmentations. This induces a local similarity score at every point in the segmentation and can in turn be accumulated in a principled information-theoretic way into a global similarity score of the entire segmentation. Experiments are conducted on benchmark images from the Berkeley segmentation database and our own database. Results show that the proposed measure can faithfully reflect the perceptual qualities of the segmentations.

SU-E-J-105: Applying Fuzzy Similarity Index and Ground Truth Fuzzy Contour to Evaluate the Segmentation Accuracy

Purpose: To evaluate the segmentation accuracy by using our novel Fuzzy Similarity Index (FSI) and Ground Truth Fuzzy Contour (GTFC) with the consideration of inter-and intra-observer variation Methods: We developed GTFC to build consensus truth segmentation(contour) and FSI to score segmentation for an objective and quantitative evaluation of in-vivo medical image segmentation. GTFC is built by applying Fuzzy theory to consider with inter-and intra-observer variation. GTFC has the Fuzzy Membership Function(FMFn) which can assign a weight to each expert depending on their experience, unlike STAPLE. By using GTFC, we formulate a quantitative scoring index to evaluate the segmentation accuracy.When a test segmentation is evaluated, we calculate the membership value of FMFn at every point in the test segmentation(contour). Then, we can make a distribution of membership value as Membership Score Histogram(MSH). We enhanced FSI to make more responsive. While generating a single value index(FSI) from MSH, we adopt the strategy of penalizing lower membership values. The resultant FSI equation is formulated by combining MSH with a penalty constant. We tested the FSI by applying to a brain case. Ten experts segmented a region in the brain and six non-experts independently delineated the same place. GTFC was created from segmentations of expert to evaluate the accuracy of segmentations of non-experts. Then, we calculated FSI after making a MSH per test segmentation. Results: The order in higher similarity to GTFC is non-expert 6, 1, 5, 2, 3, and 4. Non-expert 2, 3, and 4 are significantly deviated from GTFC. Their FSIs are 0.845, 0.476, 0.125, 0.085, 0.078, and 0.005, respectively. Conclusions:FSI can sensitively reflect the accuracy of test segmentation. It can be used to develop unbiased educational tools or credential process for clinicians. It can be also used to evaluate the performance of automated segmentation tools.

Quantification and visualization of carotid segmentation accuracy and precision using a 2D standardized carotid map

This paper describes a framework for vascular image segmentation evaluation. Since the size of vessel wall and plaque burden is defined by the lumen and wall boundaries in vascular segmentation, these two boundaries should be considered as a pair in statistical evaluation of a segmentation algorithm. This work proposed statistical metrics to evaluate the difference of local vessel wall thickness (VWT) produced by manual and algorithm-based semi-automatic segmentation methods (ΔT) with the local segmentation standard deviation of the wall and lumen boundaries considered. ΔT was further approximately decomposed into the local wall and lumen boundary differences (ΔW and ΔL respectively) in order to provide information regarding which of the wall and lumen segmentation errors contribute more to the VWT difference. In this study, the lumen and wall boundaries in 3D carotid ultrasound images acquired for 21 subjects were each segmented five times manually and by a level-set segmentation algorithm. The (absolute) difference measures (i.e., ΔT, ΔW, ΔL and their absolute values) and the pooled local standard deviation of manually and algorithmically segmented wall and lumen boundaries were computed for each subject and represented in a 2D standardized map. The local accuracy and variability of the segmentation algorithm at each point can be quantified by the average of these metrics for the whole group of subjects and visualized on the 2D standardized map. Based on the results shown on the 2D standardized map, a variety of strategies, such as adding anchor points and adjusting weights of different forces in the algorithm, can be introduced to improve the accuracy and variability of the algorithm.

Cardiac imaging research group. Results and future works

This paper presents the projects of the cardiac imaging research group, which is part of the GdR Stic-Santé. Four scientific meeting days have been organized during the period 2011–2012. These were dedicated to left ventricular remodeling, aortic and valvular mechanics, image segmentation evaluation and cardiac image segmentation. New prospects focus on the intensive use of realistic simulations and the constitution of databases of shareable images in order to compare in similar conditions different algorithms dedicated to cardiovascular image analysis.

An LG-graph-based early evaluation of segmented images

Image segmentation is one of the first important parts of image analysis and understanding. Evaluation of image segmentation, however, is a very difficult task, mainly because it requires human intervention and interpretation. In this work, we propose a blind reference evaluation scheme based on regional local–global (RLG) graphs, which aims at measuring the amount and distribution of detail in images produced by segmentation algorithms. The main idea derives from the field of image understanding, where image segmentation is often used as a tool for scene interpretation and object recognition. Evaluation here derives from summarization of the structural information content and not from the assessment of performance after comparisons with a golden standard. Results show measurements for segmented images acquired from three segmentation algorithms, applied on different types of images (human faces/bodies, natural environments and structures (buildings)).

Semivariogram-Based Spatial Bandwidth Selection for Remote Sensing Image Segmentation With Mean-Shift Algorithm

Image segmentation is a key procedure that partitions an image into homogeneous parcels in object-based image analysis (OBIA). Scale selection in image segmentation is always difficult for high-performance OBIA. This letter is aimed at scale selection before segmentation in OBIA and proposes a spatial statistics-based spatial bandwidth selection method based on mean-shift segmentation. This study uses Ikonos and Quickbird panchromatic images as the experimental data and then computes their semivariances to select the optimal spatial bandwidth for mean-shift segmentation. To validate this method and interpret the relationship between the semivariances and segmentation scale, this letter implements an image segmentation evaluation based on the homogeneity within and the heterogeneity between the segmentation parcels. The evaluation results basically support the proposed scale selection method based on the semivariogram. Consequently, the semivariogram-based spatial bandwidth selection method is practically meaningful for pre-estimating the appropriate scale and thus contributes to improving the performance and efficiency of OBIA.

A Novel Evaluation Method Based on Entropy for Image Segmentation

Image segmentation evaluation is still a hotspot problem. Various methods of image segmentation evaluation have been proposed. Amongst all the evaluation approaches, Segmentation Entropy Quantitative Assessment (SEQA) is one of the most popular methods. In this paper, segmentation entropy is proposed. In experiments, some standard images which are segmented by multi-level thresholds are tested and used to conclude the characteristics of SEQA, including its application conditions, advantages and disadvantages in image segmentation evaluation.

Database of human segmented images and its application in boundary detection

This study presents a database that can be used for boundary detection and image segmentation evaluation. Images in our database are based on image pairs instead of single images, which is a remarkable difference from existing databases. This characteristic will greatly facilitate the algorithms combining low-level cues such as brightness, colour and texture with depth information generated from the image pairs to conduct boundary detection and image segmentation tasks. Besides, the authors have put the presented database to use by comparing their proposed boundary detection method with the Berkeley detector and Edison detector. Experimental results show that their method has a better performance.

Performance Evaluation of Range Image Segmentation Based on Surface Fitting Method

Performance Evaluation of Range Image Segmentation Based on Surface Fitting Method

Unsupervised image segmentation evaluation and refinement using a multi-scale approach

In this study, a multi-scale approach is used to improve the segmentation of a high spatial resolution (30 cm) color infrared image of a residential area. First, a series of 25 image segmentations are performed in Definiens Professional 5 using different scale parameters. The optimal image segmentation is identified using an unsupervised evaluation method of segmentation quality that takes into account global intra-segment and inter-segment heterogeneity measures (weighted variance and Moran’s I, respectively). Once the optimal segmentation is determined, under-segmented and over-segmented regions in this segmentation are identified using local heterogeneity measures (variance and Local Moran’s I). The under- and over-segmented regions are refined by (1) further segmenting under-segmented regions at finer scales, and (2) merging over-segmented regions with spectrally similar neighbors. This process leads to the creation of several segmentations consisting of segments generated at three different segmentation scales. Comparison of single- and multi-scale segmentations shows that identifying and refining under- and over-segmented regions using local statistics can improve global segmentation results.

Maximizing a Sum Related to Image Segmentation Evaluation

Consider the set S of points in the plane consisting of the ordered pairs (i, j ), where $1 \leqslant i \leqslant m$ and $1 \leqslant j \leqslant n$ . A problem related to the study of segmentation evaluation of visual images concerns finding a permutation ? of the points of S for which the sum $$ \sum\limits_{s \in S}d(s, \sigma(s)) $$ is maximal among all possible permutations of S, where d denotes the Euclidean metric. In this note, we show that this maximum is achieved by exactly those permutations of S for which the line joining the points s and ?(s) passes through the point $(\frac{m+1}{2},\frac{n+1}{2})$ for all s???S. In fact, the result applies to any point-symmetric set in any dimension for all L p metrics, 1?<?p?<??. In addition, we provide asymptotic estimates as m and n grow large for the actual maximum value achieved by the above sum.

Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging

We propose an automatic hybrid image segmentation model that integrates the statistical expectation maximization (EM) model and the spatial pulse coupled neural network (PCNN) for brain magnetic resonance imaging (MRI) segmentation. In addition, an adaptive mechanism is developed to fine tune the PCNN parameters. The EM model serves two functions: evaluation of the PCNN image segmentation and adaptive adjustment of the PCNN parameters for optimal segmentation. To evaluate the performance of the adaptive EM–PCNN, we use it to segment MR brain image into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The performance of the adaptive EM–PCNN is compared with that of the non-adaptive EM–PCNN, EM, and Bias Corrected Fuzzy C-Means (BCFCM) algorithms. The result is four sets of boundaries for the GM and the brain parenchyma (GM + WM), the two regions of most interest in medical research and clinical applications. Each set of boundaries is compared with the golden standard to evaluate the segmentation performance. The adaptive EM–PCNN significantly outperforms the non-adaptive EM–PCNN, EM, and BCFCM algorithms in gray mater segmentation. In brain parenchyma segmentation, the adaptive EM–PCNN significantly outperforms the BCFCM only. However, the adaptive EM–PCNN is better than the non-adaptive EM–PCNN and EM on average. We conclude that of the three approaches, the adaptive EM–PCNN yields the best results for gray matter and brain parenchyma segmentation.

A method for multi-spectral image segmentation evaluation based on synthetic images

A general framework for testing the quality of the segmentation of a multi-spectral satellite image is proposed. The method is based on the production of synthetic images with the spectral characteristics of the image pixels extracted from a signature multi-spectral image. The knowledge of the location of objects in the synthetic image provides a reference segmentation, which allows for a quantitative evaluation of the quality provided by a segmentation algorithm. The Hammoude metric and three external similarity indices (Rand, Corrected Rand, and Jaccard) were chosen to perform this evaluation, but other metrics can also be used. The proposed methodology can be used for any type of satellite image (or multi-spectral image), set of land cover types, and segmentation algorithms. A practical application was carried out to illustrate the value of the proposed method. A SPOT satellite image was used to extract the spectral signature of 8 land cover types. Three test images were produced using the 8 land cover classes and two different 5 class sub-sets. The segmentation results provided by a standard algorithm were compared with the reference or expected segmentation. The results clearly indicate that the quality of a segmentation obtained from a multi-spectral image not only depends on the geometric properties of the objects present in the image, but also on their spectral characteristics. The results suggest that a specific evaluation should be carried out for each particular experiment, as the segmentation results are very dependent on the choice of land cover types.