Image classification based on color and texture analysis

BACKGROUND: Gynecological diseases threaten women’s health, and vaginal microecological testing is a common method for detecting gynecological diseases. Efficient and accurate microecological testing methods have always been the goal pursued by gynecologists. OBJECTIVE: In order to automatically identify different types of microbial images in vaginal micromorphology detection, this paper proposes a vaginal microecological image recognition method based on Gabor texture analysis combined with long and short-term memory network (LSTM) model. METHOD: Firstly, we denoise the microecological morphological im-ages, which selects the area of interest and sets the label of the microorganism according to the doctors label. Secondly, texture analysis is carried out for the region of interest, which uses Gabor filters with 8 directions and 5 scales to filter the region of interest to extract the texture features on the image. Comparing the differences between different microbial image features, and screening suitable features to reduce the number of features. Then, we design an LSTM model to analyze the relationship of image features in different categories of microorganisms. Finally, we use the full connection layer and Softmax function to realize the automatic recognition of different microbial images. RESULTS: The experimental results show that the image classification accuracy of 8 common microorganisms is 81.26%. CONCLUSION: Texture analysis combined with LSTM network strategy can identify different kinds of vaginal micro ecological images. Gabor-LSTM model has better classification effect on imbalanced data sets.

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A study of lacunarity-based texture analysis approaches to improve urban image classification

Computers Environment and Urban Systems ◽

10.1016/j.compenvurbsys.2005.01.007 ◽

2005 ◽

Vol 29 (5) ◽

pp. 501-523 ◽

Cited By ~ 56

Author(s):

Soe Win Myint ◽

Nina Lam

Keyword(s):

Image Classification ◽

Texture Analysis ◽

Urban Image

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A Fusion-Based Approach for Breast Ultrasound Image Classification Using Multiple-ROI Texture and Morphological Analyses

Computational and Mathematical Methods in Medicine ◽

10.1155/2016/6740956 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 10

Author(s):

Mohammad I. Daoud ◽

Tariq M. Bdair ◽

Mahasen Al-Najar ◽

Rami Alazrai

Keyword(s):

Image Classification ◽

Texture Analysis ◽

Morphological Analysis ◽

Malignant Tumors ◽

Probabilistic Approach ◽

Ultrasound Image ◽

Breast Ultrasound ◽

Support Vector ◽

Specificity And Sensitivity ◽

Tumor Class

Ultrasound imaging is commonly used for breast cancer diagnosis, but accurate interpretation of breast ultrasound (BUS) images is often challenging and operator-dependent. Computer-aided diagnosis (CAD) systems can be employed to provide the radiologists with a second opinion to improve the diagnosis accuracy. In this study, a new CAD system is developed to enable accurate BUS image classification. In particular, an improved texture analysis is introduced, in which the tumor is divided into a set of nonoverlapping regions of interest (ROIs). Each ROI is analyzed using gray-level cooccurrence matrix features and a support vector machine classifier to estimate its tumor class indicator. The tumor class indicators of all ROIs are combined using a voting mechanism to estimate the tumor class. In addition, morphological analysis is employed to classify the tumor. A probabilistic approach is used to fuse the classification results of the multiple-ROI texture analysis and morphological analysis. The proposed approach is applied to classify 110 BUS images that include 64 benign and 46 malignant tumors. The accuracy, specificity, and sensitivity obtained using the proposed approach are 98.2%, 98.4%, and 97.8%, respectively. These results demonstrate that the proposed approach can effectively be used to differentiate benign and malignant tumors.

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