Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics.
In this paper, the authors have made an attempt to classify the placenta based on the intensity level of histogram of the ultrasound images of placenta. The medical images are usually low in resolution. Specialized tools are required to assist the medical experts in medical image diagnosis and for further treatment. The image histogram is used to classify the ultrasound images of placenta into normal and abnormal placenta using k nearest neighbor classifier. It is further used to analyze the complications of gestational diabetes mellitus on the growth of the placenta.
In the medical domain, one of the major challenges faced by the medical experts is the extraction of critical information for medical diagnosis. Specialized tools are necessary to assist the experts in diagnosing the diseases. Information retrieval is difficult in the case of ultrasound medical images due to its low resolution making diagnosis difficult. Gestational diabetes is a form of diabetes, which affects pregnant women. It is believed that the hormones produced during pregnancy reduce a woman's receptivity to insulin, leading to high blood sugar levels. The duration of departures from normogycemia in maternal diabetes is the critical factor. The earlier detection of GDM occurs, the lesser the influence on placental development, which indirectly accounts for fetal growth and metabolism. This pilot study involves the feasibility for classifying the ultrasound images of placenta with complicating diabetes based on placenta thickness using statistical textural features
Medical Diagnosis is the utmost need of an hour. Gestational Diabetics in women represents the second leading cause of yielding children born with birth defects. The ultrasound images are usually low in resolution making diagnosis difficult. Specialized tools are required to assist the medical experts to categorize and diagnose diseases to accuracy. If the anomalies in the ultrasound images are detected in the preliminary screening of placenta, fetal loss could be minimized. This pilot study was carried out to find the feasibility for detecting anomalies in placental growth due to the implications of gestational diabetics by considering the stereo image mapping based on wavelet analysis for 2D reconstruction. The research uses waveletbased methods to extract features from the ultrasonic images of placenta. The shape of the placenta is generated using the Back Propagation Network. Euclidean Distance Classifier is used for classifying the ultrasonic images of placenta.
Medical diagnosis is the major challenge faced by the medical experts. Highly specialized tools are necessary to assist the experts in diagnosing the diseases. Gestational Diabetes Mellitus is a condition in pregnant women which increases the blood sugar levels. It complicates the pregnancy by affecting the placental growth. The ultrasound screening of placenta in the initial stages of gestation helps to identify the complication induced by GDM on the placental development which accounts for the fetal growth. This work focus on the classification of ultrasound placenta images into normal and abnormal images based on statistical measurements. The ultrasound images are usually low in resolution which may lead to loss of characteristic features of the ultrasound images. The placenta images obtained in an ultrasound examination is stereo mapped to reconstruct the placenta structure from the ultrasound images. The dimensionality reduction is done on stereo mapped placenta images using wavelet decomposition. The ultrasound placenta image is segmented using watershed approach to obtain the statistical measurements of the stereo mapped placenta images. Using the statistical measurements, the ultrasound placenta images are then classified as normal and abnormal using Back Propagation neural networks.
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