Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics’ average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors’ regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation’s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).

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SummaryThis paper investigates a vital issue in wireless communication systems, which is the modulation classification. A proposed framework for modulation classification based on deep learning (DL) is presented in the presence of adjacent channel interference (ACI). This framework begins with the generation of constellation diagrams from the received data. These constellation diagrams are fed to convolutional neural networks (CNNs) for modulation classification. The objective of this process is to eliminate the manual feature extraction from the received data and make feature extraction process as a built‐in step with CNNs. Three types of CNNs are considered in this paper and compared for this objective. These types are AlexNet, VGG‐16, and VGG‐19. The proposed classifier is applied on Rayliegh and Rician fading channels.

An accurate technique for breast tumor segmentation is a critical step for monitoring and quantifying breast cancer. The fully automated tumor segmentation in mammograms presents many challenges related to characteristics of an image. In this paper, a hybrid segmentation algorithm, which combines the watershed transform and level set techniques, is proposed. Since watershed segmentation is based on pixel density variation that is present in all mass tumors, it was fairly successful in locating tumors under all conditions. However it is very sensitive to small variations of the image magnitude and consequently the number of generated regions is undesirably large and the segmented boundaries are not smooth enough. Meanwhile Level set methods offer a powerful approach for the medical image segmentation since it can handle any of the cavities, concavities, convolution, splitting or merging. However, this method requires specifying initial curves and can only provide good results if these curves are placed near symmetrically with respect to the object boundary. In our proposed technique a watershed segmentation algorithm was developed to initially locate breast mass tumor candidates. In order to facilitate and improve the detection step, the segmentation results is treated as the initial localization of the desired contour, and used in the following level set method, which provides closed, smoothed and accurately localized contours or surfaces. Experimental results show the significant improvement of the final segmentation accuracy. General TermsImage Processing. KeywordsWatershed Segmentation; Breast Cancer Mammogram Detection. Image segmentation is a process that partitions an image into its constituent regions or objects. Effective segmentation of complex images is one of the most difficult tasks in image processing. Various image segmentation algorithms have been proposed to achieve efficient and accurate results. Among these algorithms, watershed segmentation is a particularly attractive method. The major idea of watershed segmentation is based on the concept of topographic representation of image intensity. Meanwhile, watershed segmentation also embodies other principal image segmentation methods including discontinuity detection, thresholding and region processing. Because of these factors, watershed segmentation displays more effectiveness and stableness than other segmentation algorithms [6]. INTRODUCTIONIn this paper, an algorithm belonging to the category of hybrid techniques is proposed, since it results from the integration of morphological watershed transform and level set method [7], and develops robust and flexible object segmentation approach. The output of the watershed detection function is used as rough approximation of the desired regions in the image, and guide for the initial location of the seed points used in the following level set method. Experimental results show that this hybrid method can solve the weakness of each method, and provide accurate, smoothed segmentation results. THE ...

This photoacoustic imaging (PAI) in medicine paper started with an introduction to PAI and the famous photoacoustic techniques including photoacoustic tomography (PAT), multispectral optoacoustic tomography (MSOT), photoacoustic microscopy (PAM), raster-scan optoacoustic mesoscopy (RSOM), and photoacoustic elastography (PAE). A modest review about noncontact laser ultrasound (LUS), having the advantage of operator-independent image quality, has been also demonstrated. A concise review of most of PAI's medical applications is demonstrated including cancer screening (for breast, thyroid, ovarian, prostate, lung, and skin), tissue oxygenation measurements, brain imaging, imaging-guided surgery (IGS), and the guidance of high intensity focused ultrasound (HIFU). Some safety considerations contributed with medical ultrasound and lasers have been then presented. In conclusion, more scientific and clinical development in the field of PAI is expected, and an increase in approved devices that utilize PAI's techniques in medical applications is also expected to serve wide sectors of medicine, whether diagnostic or therapeutic.

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