LEADER 04303nam 22005775 450 001 9910861092303321 005 20250808083334.0 010 $a3-031-57279-3 024 7 $a10.1007/978-3-031-57279-1 035 $a(MiAaPQ)EBC31343080 035 $a(Au-PeEL)EBL31343080 035 $a(CKB)32063088100041 035 $a(DE-He213)978-3-031-57279-1 035 $a(EXLCZ)9932063088100041 100 $a20240515d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFeature Extraction in Medical Image Retrieval $eA New Design of Wavelet Filter Banks /$fby Aswini Kumar Samantaray, Amol D. Rahulkar 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (162 pages) 311 08$a3-031-57278-5 327 $aContent 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. 330 $aMedical 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. 606 $aImage processing 606 $aBiomedical engineering 606 $aMaterials$xAnalysis 606 $aImaging systems 606 $aImage Processing 606 $aMedical and Health Technologies 606 $aImaging Techniques 615 0$aImage processing. 615 0$aBiomedical engineering. 615 0$aMaterials$xAnalysis. 615 0$aImaging systems. 615 14$aImage Processing. 615 24$aMedical and Health Technologies. 615 24$aImaging Techniques. 676 $a621,382 700 $aSamantaray$b Aswini Kumar$01739384 701 $aRahulkar$b Amol D$0999579 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910861092303321 996 $aFeature Extraction in Medical Image Retrieval$94163419 997 $aUNINA