Detecting Abnormalities in Colonoscopic Images by Textural Description and Neural Networks
نویسندگان
چکیده
In this paper the performance of a simple scheme for the discrimination of different texture regions in colonoscopic images is investigated. The proposed scheme uses textural descriptors based on second order gray level statistics and employs a multilayer feedforward neural network to discriminate among normal and cancer regions. Preliminary results indicate that this scheme is capable of detecting abnormalities within the same image with high accuracy. It can be also successfully applied on different images to detect abnormalities that belong to different cancer types.
منابع مشابه
A New Method for Detecting Ships in Low Size and Low Contrast Marine Images: Using Deep Stacked Extreme Learning Machines
Detecting ships in marine images is an essential problem in maritime surveillance systems. Although several types of deep neural networks have almost ubiquitously used for this purpose, but the performance of such networks greatly drops when they are exposed to low size and low contrast images which have been captured by passive monitoring systems. On the other hand factors such as sea waves, c...
متن کاملOn the use of Textural Features and Neural Networks for Leaf Recognition
for recognizing various types of plants, so automatic image recognition algorithms can extract to classify plant species and apply these features. Fast and accurate recognition of plants can have a significant impact on biodiversity management and increasing the effectiveness of the studies in this regard. These automatic methods have involved the development of recognition techniques and digi...
متن کاملTumor detection in colonoscopic images using hybrid methods for on-line neural network training
In this paper the effectiveness of a new Hybrid Evolutionary Algorithm in on-line Neural Network training for tumor detection is investigated. To this end, a Lamarck-inspired combination of Evolutionary Algorithms and Stochastic Gradient Descent is proposed. The Evolutionary Algorithm works on the termination point of the Stochastic Gradient Descent. Thus, the method consists in a Stochastic Gr...
متن کاملDetecting Abnormalities in Endoscopic Capsule Images Using Color Wavelet Features and Feed-forward Neural Networks
This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to encode textural information is named color wavelet covariance (CWC)...
متن کاملAircraft Visual Identification by Neural Networks
In the present paper, an efficient method for three dimensional aircraft pattern recognition is introduced. In this method, a set of simple area based features extracted from silhouette of aerial vehicles are used to recognize an aircraft type from its optical or infrared images taken by a CCD camera or a FLIR sensor. These images can be taken from any direction and distance relative to the fly...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1999