A Novel Color Reduction Based Image Segmentation Technique For Detection Of Cancerous Region in Breast Thermograms

Segmentation of an image into its components plays an important role in most of the image processing applications. In this article an important application of image processing in determination of Breast Cancer is studied, and A Novel Image Segmentation Technique is proposed in order to determine Cancer in Breast Thermograms. First, this image is converted from RGB to color space HSV. Then Breast shape is extracted by ACM algorithm. Finally, the image has segmented using Color Reduction Based algorithm. Experimental results on the acquired images show Accuracy of the proposed algorithm on the acquired images is over 90% for healthy pixels and defected ones

Pomegranate MR image analysis using fuzzy clustering algorithms

In this paper, the process of the pomegranate magnetic resonance (MR) images was studied.  Its internal structure is composed of tissue and seeds, which indicate the dependency between the maturity and internal quality.  The latter properties are important in pomegranate’s sorting and cannot be measured manually.  In this paper, an automatic algorithm was proposed to segment the internal structure of pomegranates.  Since the intensities of the calyx and stem of the pomegranate MR image are closely related to that of the soft tissue, their corresponding pixels are therefore labeled in the same class of the internal soft tissues.  In order to solve this problem, the exact shape of the pomegranate is first extracted from the background of the image using active contour models (ACMs).  Then, the stem and calyx are removed using morphological filters.  We have also proposed an improved version of the fuzzy c-means algorithm (FCM), the spatial FCM (SFCM), for segmentation of MR images of pomegranate.  SFCM is realized by incorporating the spatial neighborhood information into the standard FCM and modifying the membership weighting of each cluster.  SFCM employs spatial information of adjacent pixels leading to an improvement of the results.  It thus outperforms other techniques like FCM, even in the presence of Gaussian, salt and pepper, and speckle noises. Keywords: MRI, pomegranate, image segmentation, spatial fuzzy c-means, morphological filter&nbsp

Automatic Facial Skin Segmentation Using Possibilistic C-Means Algorithm for Evaluation of Facial Surgeries

Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy C-Means (FCM) clustering algorithm doesn't work appropriately for noisy images and outliers, in this paper we exploit Possibilistic C-Means (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region's area error (0.045) for the proposed algorithm

Automatic Facial Skin Segmentation Using Possibilistic C-Means Algorithm for Evaluation of Facial Surgeries

Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy C-Means (FCM) clustering algorithm doesn't work appropriately for noisy images and outliers, in this paper we exploit Possibilistic C-Means (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region's area error (0.045) for the proposed algorithm

Automatic Facial Skin Segmentation Using Possibilistic C-Means Algorithm for Evaluation of Facial Surgeries

Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy C-Means (FCM) clustering algorithm doesn't work appropriately for noisy images and outliers, in this paper we exploit Possibilistic C-Means (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region's area error (0.045) for the proposed algorithm

New Design and Implementation of a Solar Car of the American University of Ras Al Khaimah: Electrical Vision

This paper explains a full design and implementation process of a feasible solar car as an effective alternative to the gasoline powered car. A solar car is independent of fossil fuels, and would entirely eliminate emissions. Comparing to the previous manufactured solar cars which were characterized by expensive, one seat driver and unfeasible, the presented solution in this study develops a commercially feasible version of a solar car. The structure’s mass and passengers’ mass are considered to calculate the required electrical power for the car to be able to reach the target speed at 100 km/h. A three photovoltaic panels of 320 W are parallel connected as a photovoltaic array to charge a lithium ion battery bank of 48 V and 200 Ah during the day hours. The testing of the implemented car guarantees the successful and flexible design and promises an effective commercial prototype of solar car. The presented work is done in the American University of Ras Al Khaimah

Development of a new sequential block finding strategy for detection of conserved sequences in riboswitches

Introduction: Some non-coding RNAs have an important role in the regulation of gene expression and consequently cellular function. Riboswitches are examples of these regulatory RNAs. Riboswitches are classified into various families according to sequential and structural similarities. Methods: In this study, a block finder algorithm for identification of frequently appearing sequential blocks in five families of riboswitches from Rfam 12.0 database, without the use of alignment methods, was developed. Results: The developed program identified 21 frequently appearing blocks in five families of riboswitches. Conclusion: Comparison of the results of the proposed algorithm with those of sequential alignment methods revealed that our method can recognize most of the patterns present in conserved areas of individual riboswitch families and determine them as specific blocks, implying potential of the developed program as a platform for further studies and developments

Classification of seed members of five riboswitch families as short sequences based on the features extracted by Block Location-Based Feature Extraction (BLBFE) method

Introduction: Riboswitches are short regulatory elements generally found in the untranslated regions of prokaryotes’ mRNAs and classified into several families. Due to the binding possibility between riboswitches and antibiotics, their usage as engineered regulatory elements and also their evolutionary contribution, the need for bioinformatics tools of riboswitch detection is increasing. We have previously introduced an alignment independent algorithm for the identification of frequent sequential blocks in the families of riboswitches. Herein, we report the application of block location-based feature extraction strategy (BLBFE), which uses the locations of detected blocks on riboswitch sequences as features for classification of seed sequences. Besides, mono- and dinucleotide frequencies, k-mer, DAC, DCC, DACC, PC-PseDNC-General and SC-PseDNC-General methods as some feature extraction strategies were investigated. Methods: The classifiers of the Decision tree, KNN, LDA, and Naïve Bayes, as well as k-fold cross-validation, were employed for all methods of feature extraction to compare their performances based on the criteria of accuracy, sensitivity, specificity, and f-score performance measures. Results: The outcome of the study showed that the BLBFE strategy classified the riboswitches indicating 87.65% average correct classification rate (CCR). Moreover, the performance of the proposed feature extraction method was confirmed with average values of 94.31%, 85.01%, 95.45% and 85.38% for accuracy, sensitivity, specificity, and f-score, respectively. Conclusion: Our result approved the performance of the BLBFE strategy in the classification and discrimination of the riboswitch groups showing remarkable higher values of CCR, accuracy, sensitivity, specificity and f-score relative to previously studied feature extraction methods

Bax/Bcl-2 Cascade Is Regulated by the EGFR Pathway: Therapeutic Targeting of Non-Small Cell Lung Cancer

Non-small cell lung carcinoma (NSCLC) comprises 80%–85% of lung cancer cases. EGFR is involved in several cancer developments, including NSCLC. The EGFR pathway regulates the Bax/Bcl-2 cascade in NSCLC. Increasing understanding of the molecular mechanisms of fundamental tumor progression has guided the development of numerous antitumor drugs. The development and improvement of rationally planned inhibitors and agents targeting particular cellular and biological pathways in cancer have been signified as a most important paradigm shift in the strategy to treat and manage lung cancer. Newer approaches and novel chemotherapeutic agents are required to accompany present cancer therapies for improving efficiency. Using natural products as a drug with an effective delivery system may benefit therapeutics. Naturally originated compounds such as phytochemicals provide crucial sources for novel agents/drugs and resources for tumor therapy. Applying the small-molecule inhibitors (SMIs)/phytochemicals has led to potent preclinical discoveries in various human tumor preclinical models, including lung cancer. In this review, we summarize recent information on the molecular mechanisms of the Bax/Bcl-2 cascade and EGFR pathway in NSCLC and target them for therapeutic implications. We further described the therapeutic potential of Bax/Bcl-2/EGFR SMIs, mainly those with more potent and selectivity, including gefitinib, EGCG, ABT-737, thymoquinone, quercetin, and venetoclax. In addition, we explained the targeting EGFR pathway and ongoing in vitro and in vivo and clinical investigations in NSCLC. Exploration of such inhibitors facilitates the future treatment and management of NSCLC