04598nam a22005655i 4500 978-3-031-57279-1 DE-He213 20251030112043.0 cr nn 008mamaa 240515s2024 sz | s |||| 0|eng d 9783031572791 978-3-031-57279-1 10.1007/978-3-031-57279-1 doi TA1637-1638 TJF bicssc UYT bicssc COM012050 bisacsh TJF thema UYT thema Samantaray, Aswini Kumar. author. aut http://id.loc.gov/vocabulary/relators/aut 76860 Feature Extraction in Medical Image Retrieval [electronic resource] : A New Design of Wavelet Filter Banks / by Aswini Kumar Samantaray, Amol D. Rahulkar. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XV, 155 p. 55 illus., 10 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Content Based Medical Image Retrieval -- Fundamentals of Wavelet Filter Banks -- Fundamentals of Gabor Wavelet Filter Banks -- A Family of Multiplier Free Orthogonal Wavelet Filter Banks -- Design of Symmetric and Completely Dyadic db-4 Wavelet Filter Bank -- Design of Dyadic Gabor Wavelet Filter Banks -- Design of Adaptive Gabor Wavelet Filter Banks. Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in creation of image databases. These repositories contain images from a diverse range of modalities, multidimensional as well as co-aligned multimodality images. These image collections offer opportunity for evidence-based diagnosis, teaching, and research. Advances in medical image analysis over last two decades shows there are now many algorithms and ideas available that allow to address medical image analysis tasks in commercial solutions with sufficient performance in terms of accuracy, reliability and speed. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. This book emphasizes the design of wavelet filter-banks as efficient and effective feature descriptors for medical image retrieval. Firstly, a generalized novel design of a family of multiplier-free orthogonal wavelet filter-banks is presented. In this, the dyadic filter coefficients are obtained based on double-shifting orthogonality property with allowable deviation from original filter coefficients. Next, a low complex symmetric Daub-4 orthogonal wavelet filter-bank is presented. This is achieved by slightly altering the perfect reconstruction condition to make designed filter-bank symmetric and to obtain dyadic filter coefficients. In third contribution, the first dyadic Gabor wavelet filter-bank is presented based on slight alteration in orientation parameter without disturbing remaining Gabor wavelet parameters. In addition, a novel feature descriptor based on the design of adaptive Gabor wavelet filter-bank is presented. The use of Maximum likelihood estimation is suggested to measure the similarity between the feature vectors of heterogeneous medical images. The performance of the suggested methods is evaluated on three different publicly available databases namely NEMA, OASIS and EXACT09. The performance in terms of average retrieval precision, average retrieval recall and computational time are compared with well-known existing methods. Image processing. Biomedical engineering. Materials Analysis. Imaging systems. 68001 Image Processing. Medical and Health Technologies. Imaging Techniques. 68003 Rahulkar, Amol D. author. aut http://id.loc.gov/vocabulary/relators/aut 76861 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031572784 Printed edition: 9783031572807 Printed edition: 9783031572814 https://doi.org/10.1007/978-3-031-57279-1 ZDB-2-SCS ZDB-2-SXCS 59874 59874