Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) is needed for the neuroimaging applications. In this paper, performance evaluation of three widely used brain segmentation software packages SPM8, FSL and Brainsuite is presented. Segmentation with SPM8 has been performed in three frameworks: i) default segmentation, ii) SPM8 New-segmentation and iii) modified version using hidden Markov random field as implemented in SPM8-VBM toolbox. The accuracy of the segmented GM, WM and CSF and the robustness of the tools against changes of image quality has been assessed using Brainweb simulated MR images and IBSR real MR images. The calculated similarity between the segmented tissues using different tools and corresponding ground truth shows variations in segmentation results.Comparison with Existing A few studies has investigated GM, WM and CSF segmentation. In these studies, the skull stripping and bias correction are performed separately and they just evaluated the segmentation. Thus, in this study, assessment of complete segmentation framework consisting of pre-processing and segmentation of these packages is performed. The obtained results can assist the users in choosing an appropriate segmentation software package for the neuroimaging application of interest.

Precipitation rate and amount measurements are among the flood warning methods which have been suggested by remote sensing in recent years. Cloud type identification and classification, as basic principles of precipitation estimation methods, are usually performed using visual interpretation of satellite images. In these studies only cloud brightness temperature and albedo are used for cloud classification, while texture and shape of clouds are effective properties in cloud type detection as well. Textures and shapes of clouds are ignored in pixel base classifications. So object-oriented classification technique is a suitable approach. In this technique, in addition to cloud brightnesstemperature and albedo, textures and shapes are the major parameters. Object-orient classification method, despite its benefits, depends on segmentation accuracy. The accuracy of segmentation is scale dependent too. Therefore, optimum segmentation scale is resulting to higher accuracy of object oriented classification. In this study two NOAA/AVHRR images in two consecutive cloudy days in August 2005 are used. In the first step, additional information included brightness temperature of band 3 and 4 and cloud height produced from NOAA/AVHRR images that used in image segmentation;and bi-spectral method has been employed for training region selection. Then the negative impacts of under-segmentation errors on the potential accuracy of object-based classification were quantified by developing a new segmentation accuracy measure. In this step, scale evaluation was performed with quantifying overall effect relative to features and units in 31 scales of segmentation.The results based on a NOAA/AVHRR satellite images were the same and indicate that: (1): cloud segmentation accuracies decrease with increasing segmentation scales; and (2) the negative impacts of under-segmentation errors in cloud segmentation become significantly large at large scales. Hence, the finest scale for cloud segmentation has been defined 50 as in this scale the overall accuracy of classification was 90.5% in cloud object oriented classification.

The present study was conducted with the aim of the effects of segmentation and redundancy methods on cognitive load and vocabulary learning and comprehension of English lessons in a multimedia learning environment.The purpose of this study is an applied research and a real experimental study. The statistical population of the present study includes all people aged 14 to 16 who are enrolled in language schools in Damghan. The subject of the educational content was to explain the activity of volcanoes in the form of PowerPoint software in a maximum of 10 slides (based on the type of group) to 90 subjects who were randomly divided into six groups (15 people in each group). High segmentation / Audio presentation, High segmentation / Visual presentation, High segmentation / Redundant, Low segmentation / Audio presentation, Low segmentation / Visual presentation, Low segmentation / Redundant were assigned. The research was completely experimental (a completely random 2×3 factor design). Researcher-made test of vocabulary and comprehension learning, prior knowledge assessment and Pass Cognitive Load Scale (1992) were used to collect data. Analysis of variance was used to analyze the data.The results of analysis of variance showed that the interaction between segmentation method and redundancy method on cognitive load and learning was significant. The results of post hoc test also showed that the High segmentation / Visual presentation group reported significantly lower cognitive load and higher comprehension learning compared to other groups. no significant difference was observed in the vocabulary learning variable between High segmentation / Redundant with High segmentation / Visual presentation group. According to the obtained results, it can be said that segmentation and redundancy methods are effective in reducing cognitive load and promoting English language learning in a multimedia learning environment.

Synthetic aperture radar (SAR) sensors with various properties offer potential in various remote sensing applications, such as land cover and land use segmentation. Despite the two independent approaches of region-based segmentation and boundary-based segmentation, it isn't easy to obtain satisfactory results if either process is used in SAR images. In contrast, complementary information can be obtained using both region-based and boundary-based segmentation methods, removing existing limitations and improving results. In this research, with the help of polarimetric SAR images, a new segmentation method is presented, aiming to improve segmentation results by combining the two region-based and boundary-based approaches. From the set of superpixel methods, the Felzenszwalb method as a proposed region-based algorithm is compared with Quickshift and SLIC methods. The proposed method was able to prevent over-segmentation of the image and significantly increased the efficiency of segmentation analysis. Also, as the proposed method of boundary-based segmentation, Shannon entropy has considerably preserved the boundaries of the image segmentation compared to the two gradient-based methods, Canny and Laplacian. Comparison of the results of this method with reference data shows the total error of 10.39% and 11.25% for the first and second-time images, respectively. Compared to the performance of the other two methods, the absolute error has been decreased to 5.81% and 9.73% in the first image, and 11.16% and 13.86% in the second image, respectively. Finally, as a significant achievement of this research, integrating the two proposed segmentation algorithms improves the accuracy of polarimetric image segmentation.

Accurate segmentation of brain tissue in magnetic resonance imaging (MRI) is an important step in the analysis of brain images. There are automated methods used to segmentation the brain and minimize the disadvantages of manual segmentation, including time consuming and misinterpretations. These procedures usually involve a combination of skull removal, bias field correction, and segmentation. Therefore, segmented tissue quality assessment segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is required for the analysis of neuroimages.
This paper presents the performance evaluation of three automatic methods brain segmentation, fluid and white matter suppression [FSL, Freesurfer (FreeSurfer is an open source package for the analysis and visualization of structural, functional, and diffusion neuroimaging data from cross-sectional and longitudinal studies) and SPM12 (Statistical Parametric Mapping)]. Segmentation with SPM12 was performed on three tissue probability maps: i) threshold 0.5, ii) threshold 0.7 and iii) threshold 0.9. In order to compare and evaluate the automatic methods, the reference standard method, i.e., manual segmentation, was performed by three radiologists.
Comparison of GM, WM and CSF segmentation in MR images was performed using similarities between manual and automatic segmentation. The similarity between the segmented tissues was calculated using diagnostic criteria.
Several studies have examined the classification of GM, WM, and CSF using software packages. In these studies, different results have been obtained depending on the type of method and images used and the type of segmented tissues. In this study, the evaluation of the segmentation of these packages with reference standard method is performed. The results can help users in selecting an appropriate segmentation tool for neuroimages analysis.

Breast lesion segmentation in MR Images is one of the most important parts of clinical diagnostic tools. Pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. On the other hand, unsupervised segmentation methods need no prior knowledge and lead to low performance. However, semi-supervised learning (SSL) which uses not only a few labeled data but also a large amount of unlabeled data promises higher accuracy with less effort. In this paper, we propose a new interactive semi-supervised approach to segmentation of suspicious lesions in breast MRI. Since using a suitable classifier in this approach has an important role in its performance, in this paper, we present a semi-supervised algorithm IMPST (Improved Self_Training) which is improved version of Self-Training method and increase segmentation accuracy. Experimental results show that performance of segmentation in this approach is higher than supervised and unsupervised methods such as K.N.N, Bayesian, SVM and Fuzzy c-Means.

Customer segmentation and analyzing their behavior at fast moving costumer goods (FMGS) industries which deal with a large number of customers with a variety of characteristics cause being targeted marketing and effective communication with the customers. Segmentation، the data mining approach، led to the discovery of similar groups of customers. Usually، segmentation is done by recency، frequency and purchased volume variables in RFM model. Using proper segmentation variables affects on quality of segmentation. Analyzing the quality of Golsetan customer segments، the biggest FMCG industry in Iran، confirms the hypothesis which the recency variable is not effective in customer segmentation in FMCG industries. In this paper، purchase sequence (continuity) variable is defined as a new customer performance variable in FMCG industries. By replacing the continuity variable (C) with recency in RFM model، the quality of segmentation has been improved. Customers of Golestan Company segmented by two RFM and proposed (CFM) models. The Davis-Bouldin criterion reduced more than 11 percent and the forecast accuracy for customers cluster in artificial neural networks increased about 1 percent.

In the modern times the book Wasā'il al-Shī‘ah has been introduced among the jurisprudents and hadith scholars as the most important book that contains detrimental segmentations. Studying the characteristics of segmentation in the book Wasā'il al- Shī‘ah, this article finds and analyzes the detrimental cases of segmentation in this book and compares them with the book Jāmi` Ahādīth al- Shī‘ah with the purpose of evaluating the consequences of segmentation in this hadith collection. Exact detection of the cases of segmentation and the way it was done by the compiler, as well as the rareness of the detrimental cases of segmentation suggest the subtlety of Shaykh al-Ḥurr al-`Āmilī and the firmness of his method. With regard to cases other than the causes of issuance of hadith and transmitting the atmosphere surrounding it, the book Jāmi` Ahādīth al- Shī‘ah like Wasā'il al- Shī‘ah was prompted to segmentate the traditions. These cases represent that the detriments originating from segmentation in the book Wasā'il al-Shī`ah are very rare and can be prevented (by proper treatment).

Teeth segmentation is an important task in computer‑aided procedures and clinical diagnosis. In this paper, we propose an accurate and robust algorithm based on watershed and morphology operators for teeth and pulp segmentation and a new approach for enamel segmentation in cone‑beam computed tomography (CBCT) images. Proposed method consists of fve steps: acquiring appropriate CBCT image, image enhancement, teeth segmentation using the marker‑controlled watershed (MCW), enamel segmentation by global threshold, and fnally, utilizing the MCW for pulp segmentation. Proposed algorithms evaluated on a dataset consisting 69 patient images. Experimental results show a high accuracy and specifcity for teeth, enamel, and pulp segmentation. MCW algorithm and local threshold are accurate and robust approaches to segment tooth, enamel, and pulp tissues. Methods overcome the over‑segmentation phenomenon and artifacts reduction.

Image segmentation is a common technique in digital image processing and analysis that partitions an image into several regions or zones, frequently based on the pixels' attributes. Brain tumor segmentation is a crucial task in medical image processing. Early identification of brain tumors enhances treatment options and increases the patient's chance of survival. Brain segmentation from a significant number of MR images obtained in medical treatment is a challenging and time-consuming assignment for cancer diagnosis and other brain diseases. That is why it is crucial to establish an efficient automatic image segmentation system for the diagnosis of brain tumors and other prevalent nervous diseases. The goal of this research is to undertake a systematic review of MRI-based brain tumor segmentation approaches. Deep learning techniques have proven useful for automatic segmentation in recent years and gained prominence, as these methods produce superior results and are thus better suited to this task than other methods. Deep learning algorithms may also be used to process enormous volumes of MRI-based image data quickly and objectively. Many review papers on traditional MRI-based brain tumor image segmentation algorithms are available.