Skip to main content

Advertisement

SpringerLink
Log in

Image descriptors in radiology images: a systematic review

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Clinical decisions are sometimes based on a variety of patient’s information such as: age, weight or information extracted from image exams, among others. Depending on the nature of the disease or anatomy, clinicians can base their decisions on different image exams like mammographies, positron emission tomography scans or magnetic resonance images. However, the analysis of those exams is far from a trivial task. Over the years, the use of image descriptors—computational algorithms that present a summarized description of image regions—became an important tool to assist the clinician in such tasks. This paper presents an overview of the use of image descriptors in healthcare contexts, attending to different image exams. In the making of this review, we analyzed over 70 studies related to the application of image descriptors of different natures—e.g., intensity, texture, shape—in medical image analysis. Four imaging modalities are featured: mammography, PET, CT and MRI. Pathologies typically covered by these modalities are addressed: breast masses and microcalcifications in mammograms, head and neck cancer and Alzheimer’s disease in the case of PET images, lung nodules regarding CTs and multiple sclerosis and brain tumors in the MRI section.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Bagui SC, Bagui S, Pal K, Pal NR (2003) Breast cancer detection using rank nearest neighbor classification rules. Pattern Recognit 36(1):25–34

    Article  MATH  Google Scholar 

  • Belkasim S, Shridhar M, Ahmadi M (1991) Pattern recognition with moment invariants: a comparative study and new results. Pattern Recognit 24(12):1117–1138

    Article  Google Scholar 

  • Bicacro E, Silveira M, Marques JS (2012) Alternative feature extraction methods in 3D brain image-based diagnosis of Alzheimer’s disease. In: Proceedings of the 19th IEEE international conference on image processing (ICIP). pp 1237–1240

  • Boroczky L, Luyin Z, Lee K (2005) Feature subset selection for improving the performance of false positive reduction in lung nodule cad. In: Proceedings of the 18th IEEE symposium on computer-based medical systems. pp 85–90

  • Brown MS, McNitt-Gray MF, Goldin JG, Suh RD, Sayre JW, Aberle DR (2001) Patient-specific models for lung nodule detection and surveillance in CT images. IEEE Trans Med Imaging 20(12):1242–1250

    Article  Google Scholar 

  • Buciu I, Gacsadi A (2011) Directional features for automatic tumor classification of mammogram images. Biomed Signal Process Control 6(4):370–378

    Article  Google Scholar 

  • Cabral TM, Rangayyan RM (2012) Analysis of breast masses in mammograms. Synth Lectures Biomed Eng 7(3):1–118

    Article  Google Scholar 

  • Candès E, Demanet L, Donoho D, Ying L (2006) Fast discrete curvelet transforms. Multiscale Model Simul 5(3):861–899

    Article  MathSciNet  MATH  Google Scholar 

  • Canterakis N (1999) 3D Zernike moments and Zernike affine invariants for 3D image analysis. In: Proceedings of the 11th scandinavian conference on image analysis, pp 85–93

  • Christoyianni I, Dermatas E, Kokkinakis G (2000) Fast detection of masses in computer-aided mammography. IEEE Signal Process Mag 17(1):54–64

    Article  Google Scholar 

  • Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 25(6):1045–1057

    Article  Google Scholar 

  • Cocosco CA, Kollokian V, Kwan RK, Evans AC (1997) BrainWeb: online interface to a 3D MRI simulated brain database. Neuroimage 5(4):1045–1057

    Google Scholar 

  • Constantinidis AS, Fairhurst MC, Rahman AFR (2001) A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms. Pattern Recognit 34(8):1527–1537

    Article  MATH  Google Scholar 

  • Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05). pp 886–893

  • Davatzikos AC, Fan AY, Wu AX, Shen AD, Resnick BSM (2006) Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiol Aging 29(4):514–523

    Article  Google Scholar 

  • Depeursinge A, Sage D, Hidki A, Platon A, Poletti P, Unser M, Muller H (2007) Lung tissue classification using wavelet frames. In: Proceedings of the 29th annual international conference of the IEEE engineering in medicine and biology society. pp 6259–6262

  • Dettori L, Semler L (2007) A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography. Comput Biol Med 37(4):486–498

    Article  Google Scholar 

  • Dhawan A (1996) Analysis of mammographic microcalcifications using gray-level image structure features. IEEE Trans Med Imaging 15(3):246–259

    Article  Google Scholar 

  • Dua S, Singh H, Thompson HW (2009) Associative classification of mammograms using weighted rules. Expert Syst Appl 36(5):9250–9259

    Article  Google Scholar 

  • Eltoukhy MM, Faye I, Samir BB (2010) A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Comput Biol Med 40(4):384–391

    Article  Google Scholar 

  • Eshelman LJ (1991) The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. Found Genet Algorithms 1:265–283

    Article  Google Scholar 

  • Fan Y, Resnick SM, Wu X, Davatzikos C (2008) Structural and functional biomarkers of prodromal Alzheimer’s disease: a high-dimensional pattern classification study. Neuroimage 41(2):277–285

    Article  Google Scholar 

  • Fehr J (2007) Rotational invariant uniform local binary patterns for full 3d volume texture analysis. In: Proceedings of FinSig, 6 pp

  • Feng M, Reed TR (2007) Motion estimation in the 3-D Gabor domain. IEEE Trans Image Process 16(8):2038–2047

    Article  MathSciNet  Google Scholar 

  • Ferreira CBR, Borges DL (2003) Analysis of mammogram classification using a wavelet transform decomposition. Pattern Recognit Lett 24(7):973–982

    Article  Google Scholar 

  • Galloway MM (1975) Texture analysis using gray level run lengths. Comput Graph Image Process 4(2):172–179

    Article  Google Scholar 

  • Gerardin E, Chetelat G, Chupin M, Cuingnet R, Desgranges B, Kim H, Niethammer M, Dubois B, Lehericy S, Garnero L, Eustache F, Colliot O (2009) Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage 47(4):1476–1486

    Article  Google Scholar 

  • Guevara-López MA, Posada NG, Moura DC, Pollán RR, José M (2015) BCDR: a breast cancer digital repository. In: Proceedings of the 15th international conference on experimental mechanics. pp 1–5

  • Guliato D, de Carvalho JD, Rangayyan RM, Santiago SA (2008) Feature extraction from a signature based on the turning angle function for the classification of breast tumors. J Digit Imaging 21:129–144

    Article  Google Scholar 

  • Haralick RM, Shanmuga K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  Google Scholar 

  • Heath M, Bowyer K, Kopans D, Moore R, Jr, PK (2000) The digital database for screening mammography. In: Proceedings of the 5th international workshop on digital mammography. pp 212–218

  • Herlidou-Même S, Constans J, Carsin B, Olivie D, Eliat P, Nadal-Desbarats L, Gondry C, Rumeur EL, Idy-Peretti I, de Certaines J (2003) MRI texture analysis on texture test objects, normal brain and intracranial tumors. Magn Reson Imaging 21(9):989–993

    Article  Google Scholar 

  • Hill DLG, Batchelor PG, Holden M, Hawkes DJ (2001) Medical image registration. Phys Med Biol 46(3):R1–R45

    Article  Google Scholar 

  • Hu MK (1962) Visual-pattern recognition by moment invariants. IRE Trans Inf Theory 8(2):179–187

    Article  MATH  Google Scholar 

  • Huo Z, Giger ML, Vyborny CJ, Wolverton DE, Schmidt RA, Doi K (1998) Automated computerized classification of malignant and benign masses on digitized mammograms. Acad Radiol 5(3):155–168

    Article  Google Scholar 

  • Iftekharuddin KM, Zheng J, Islam MA, Ogg RJ (2009) Fractal-based brain tumor detection in multimodal {MRI}. Appl Math Comput 207(1):23–41

    MathSciNet  MATH  Google Scholar 

  • Jack CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Borowski B, Britson PJ, Whitwell JL, Ward C, Dale AM, Felmlee JP, Gunter JL, Hill DL, Killiany R, Schuff N, Fox-Bosetti S, Lin C, Studholme C, DeCarli CS, Krueger G, Ward HA, Metzger GJ, Scott KT, Mallozzi R, Blezek D, Levy J, Debbins JP, Fleisher AS, Albert M, Green R, Bartzokis G, Glover G, Mugler J, Weiner MW (2008) The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magnet Reson Imaging 27(4):685–691

    Article  Google Scholar 

  • Jégou H, Perronnin F, Douze M, Sánchez J, Pérez P, Schmid C (2011) Aggregating local image descriptors into compact codes. IEEE Trans Pattern Anal Mach Intell 34(9):1704–1716

    Article  Google Scholar 

  • Kato N, Fukui M, Isozaki T (2009) Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease. In: Proceedings of the SPIE 7260, medical imaging 2009: computer-aided diagnosis, vol 7260. pp 1–10

  • Kim JK, Park H (1999) Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE Trans Med Imaging 18(3):231–238

    Article  Google Scholar 

  • Kläser A, Marszalek M, Schmid C (2008) A spatio-temporal descriptor based on 3D-gradients. In: Proceedings of the British machine vision 2008 (BMVC’08), 10 pp

  • Ko JP, Betke M (2001) Chest CT: automated nodule detection and assessment of change over time-preliminary experience. Radiology 218(1):267–273

    Article  Google Scholar 

  • Loizou CP, Kyriacou EC, Seimenis I, Pantziaris M, Christodoulos C, Pattichis CS (2011) Brain white matter lesions classification in multiple sclerosis subjects for the prognosis of future disability. In: Iliadis L, Maglogiannis I, Papadopoulos H (eds) Artificial intelligence applications and innovations, IFIP advances in information and communication technology, vol 364. Springer, Berlin, pp 400–409

    Chapter  Google Scholar 

  • Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the 7th IEEE international conference on computer vision (ICCV’99). pp 1150–1157

  • Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110

    Article  Google Scholar 

  • Madabhushi A, Feldman MD, Metaxas DN, Tomaszeweski J, Chute D (2005) Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI. IEEE Trans Med Imaging 24(12):1611–1625

    Article  Google Scholar 

  • Marchette D, Priebe CE, Julin E, Rogers G, Solka JL (1994) The filtered kernel estimator. In: Proceedings of the 26th symposium on the interface. p 25

  • Mathias J, Tofts P, Losseff NA (1999) Texture analysis of spinal cord pathology in multiple sclerosis. Magn Reson Med 42(5):929–935

    Article  Google Scholar 

  • McNitt-Gray M, Wyckoff N, Sayre J, Goldin J, Aberle D (1999) The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography. Comput Med Imaging Graph 23(6):339–348

    Article  Google Scholar 

  • Meinel LA, Stolpen AH, Berbaum KS, Fajardo LL, Reinhardt JM (2007) Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system. J Magn Reson Imaging 25(1):89–95

    Article  Google Scholar 

  • Mikolajczyk K, Leibe B, Schiele B (2005) Local features for object class recognition. In: Proceedings of the 10th IEEE international conference on computer vison (ICCV’05). pp 1792–1799

  • Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630

    Article  Google Scholar 

  • Morgado PM (2012) Automated diagnosis of Alzheimer’s disease using PET images a study of alternative procedures for feature extraction and selection electrical and computer engineering. Master’s thesis, MSc thesis at Electrical and Computer Engineering Dep., Higher technical institute, Technical University of Lisbon

  • Moura DC, Guevara-López MA (2013) An evaluation of image descriptors combined with clinical data for breast cancer diagnosis. Int J Comput Assist Radiol Surg 8(4):561–574

    Article  Google Scholar 

  • Mu T, Nandi AK, Rangayyan RM (2008) Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers. J Digit Imaging 21(2):153–169

    Article  Google Scholar 

  • Murphy K, van Ginneken B, Schilham A, de Hoop B, Gietema H, Prokop M (2009) A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med Image Anal 13(5):757–770

    Article  Google Scholar 

  • Nanni L, Brahnam S, Ghidoni S, Menegatti E (2014) Region-based approaches and descriptors extracted from the co-occurrence matrix. Int J Latest Res Sci Technol 3(6):192–200

    Google Scholar 

  • Nanni L, Brahnam S, Ghidoni S, Menegatti E (2015) Improving the descriptors extracted from the co-occurrence matrix using preprocessing approaches. Expert Syst Appl 42(22):8989–9000

    Article  Google Scholar 

  • Nanni L, Brahnam S, Ghidoni S, Menegatti E, Barrier T (2013) Different approaches for extracting information from the co-occurrence matrix. PLoS One 8(12):1–9

    Article  Google Scholar 

  • Naqa IE, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D, Chaudhari S, Yang D, Schmitt M, Laforest R, Thorstad W, Deasy J (2009) Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit 42(6):1162–1171

    Article  Google Scholar 

  • Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  • Oliveira FP, Tavares JM (2014) Medical image registration: a review. Comput Methods Biomech Biomed Eng 17(2):73–93

    Article  Google Scholar 

  • Oliver A, Torrent A, Llado X, Tortajada M, Tortajada L, Sentís M, Freixenet J, Zwiggelaar R (2012) Automatic microcalcification and cluster detection for digital and digitised mammograms. Knowledge-Based Syst 28:68–75

    Article  Google Scholar 

  • Porat M, Zeevi YY (1989) Localized texture processing in vision: analysis and synthesis in the gaborian space. IEEE Trans Biomed Eng 36(1):115–129

    Article  Google Scholar 

  • Priebe C, Julin E, Rogers G, Healy D, Lu J, Solka JL, Marchette D (1994) Incorporating segmentation boundaries into the calculation of fractal dimension features. In: Proceedings of the 26th symposium on the interface. pp 52–56

  • Ramírez J, Gorriz J, Salas-Gonzalez D, Romero A, Lopez M, Alvarez I, Gomez-Río M (2013) Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features. Inf Sci 237:59–72

    Article  Google Scholar 

  • Ramos-Pollan R, Guevara-Lopez MA, Suarez-Ortega C, Díaz-Herrero G, Franco-Valiente JM, del Solar MR, de Posada NG, Vaz MAP, Loureiro J, Ramos I (2012) Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J Med Syst 36(4):2259–2269

    Article  Google Scholar 

  • Rashed EA, Ismail IA, Zaki SI (2007) Multiresolution mammogram analysis in multilevel decomposition. Pattern Recognit Lett 28(2):286–292

    Article  Google Scholar 

  • Reddy KK, Solmaz B, Yan P, Avgeropoulos NG, Rippe DJ, Shah M (2012) Confidence guided enhancing brain tumor segmentation in multi-parametric mri. In: Proceedings of the international symposium on biomedical imaging. pp 366–369

  • Rojas-Domínguez A, Nandi AK (2009) Development of tolerant features for characterization of masses in mammograms. Comput Biol Med 39(8):678–688

    Article  Google Scholar 

  • Sahiner B, Chan H, Petrick N, Helvie MA, Hadjiiski LM (2001) Improvement of mammographic mass characterization using spiculation measures and morphological features. Med Phys 28(7):1455–1465

    Article  Google Scholar 

  • Scovanner P, Ali S, Shah M (2007) A 3-dimensional sift descriptor and its application to action recognition. In: Proceedings of the 15th ACM international conference on multimedia, MM ’07. pp 357–360

  • Sharma S, Khanna P (2015) Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM. J Digit Imaging 28(1):77–90

    Article  Google Scholar 

  • Sheshadri H, Kandaswamy A (2007) Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms. Comput Med Imaging Graph 31(1):46–48

    Article  Google Scholar 

  • Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T, Kobayashi T, Komatsu K, Matsui M, Fujita H, Kedera Y, Doi K (2000) Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am J Roentgenol 174(12):71–74

    Article  Google Scholar 

  • Silveira M, Marques J (2010) Boosting alzheimer disease diagnosis using pet images. In: Proceedings of the 20th international conference on pattern recognition (ICPR). pp 2556–2559

  • Soltanian-Zadeh H, Rafiee-Rad F, Pourabdollah-Nejad SD (2004) Comparison of multiwavelet, wavelet, haralick, and shape features for microcalcification classification in mammograms. Pattern Recognit 37(10):1973–1986

    Article  Google Scholar 

  • Sotiras A, Davatzikos C, Paragios N (2013) Deformable medical image registration: a survey. IEEE Trans Med Imaging 32(7):1153–1190

    Article  Google Scholar 

  • Suckling J, Parker J, Dance DR, Astley SM, Hutt I, Boggis CRM, Ricketts I, Stamatakis E, Cerneaz N, Kok SL, Taylor P, Betal D, Savage J (1994) The mammographic image analysis society digital mammogram database. In: Proceedings of the international workshop on digital mammography. pp 211–221

  • Tahmasbi A, Saki F, Shokouhi SB (2010) Mass diagnosis in mammography images using novel ftrd features. In: Proceedings of the 17th Iranian conference of biomedical engineering (ICBME). pp 1–5

  • Tahmasbi A, Saki F, Shokouhi SB (2011) Classification of benign and malignant masses based on Zernike moments. Comput Biol Med 41(8):726–735

    Article  Google Scholar 

  • Teague MR (1980) Image-analysis via the general-theory of moments. J Opt Soc Am 70(8):920–930

    Article  MathSciNet  Google Scholar 

  • Theocharakis P, Glotsos D, Kalatzis I, Kostopoulos S, Georgiadis P, Sifaki K, Tsakouridou K, Malamas M, Delibasis G, Cavouras D, Nikiforidis G (2009) Pattern recognition system for the discrimination of multiple sclerosis from cerebral microangiopathy lesions based on texture analysis of magnetic resonance images. Magn Reson Imaging 27(3):417–422

    Article  Google Scholar 

  • Tola E, Lepetit V, Fua P (2010) Daisy: an efficient dense descriptor applied to wide baseline stereo. IEEE Trans Pattern Anal Mach Intell 32(5):815–830

    Article  Google Scholar 

  • Tsai F, Chang CK, Rau JY, Lin TH, Liu GR (2007) 3D computation of gray level co-occurrence in hyperspectral image cubes. In: International workshop on energy minimization methods in computer vision and pattern recognition. Springer, Berlin, pp 429–440

  • Unay D, Ekin A, Cetin M, Jasinschi R, Ercil A (2007) Robustness of local binary patterns in brain mr image analysis. In: Proceedings of the IEEE engineering in medicine and biology society annual conference, vol 2007. pp 2098–2101

  • Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the conference on computer vision and pattern recognition. pp 511–518

  • Wang D, Shi L, Heng PA (2009) Automatic detection of breast cancers in mammograms using structured support vector machines. Neurocomputing 72(13–15):3296–3302

    Article  Google Scholar 

  • Wiemker R, Rogalla P, Zwartkruis A, Blaffert T (2002) Computer-aided lung nodule detection on high-resolution ct data. In: Proceedings of the SPIE 4684, medical imaging 2002: image processing. pp 677–688

  • Wu B, Khong P, Chan T (2012) Automatic detection and classification of nasopharyngeal carcinoma on PET/CT with support vector machine. Int J Comput Assist Radiol Surg 7(4):635–646

    Article  Google Scholar 

  • Xu D, Lee J, Raicu D, Furst J, Channin D (2005) Texture classification of normal tissues in computed tomography. In: Proceedings of the 2005 annual meeting of the society for computer applications in radiology. Orlando, Florida

  • Yu H, Caldwell C, Mah K, Mozeg D (2009) Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning. IEEE Trans Med Imaging 28(3):374–383

    Article  Google Scholar 

  • Yu O, Mauss Y, Zollner G, Namer I, Chambron J (1999) Distinct patterns of active and non-active plaques using texture analysis on brain NMR images in multiple sclerosis patients: preliminary results. Magn Reson Imaging 17(9):1261–1267

    Article  Google Scholar 

  • Yu S, Guan L (2000) A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Trans Med Imaging 19(2):115–126

    Article  Google Scholar 

  • Zacharaki EI, Wang S, Chawla S, Yoo DS, Wolf R, Melhem ER, Davatzikos C (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62(6):1609–1618

    Article  Google Scholar 

  • Zhang J, Tong L, Wang L, Li N (2008) Texture analysis of multiple sclerosis: a comparative study. Magn Reson Imaging 26(8):1160–1166

    Article  Google Scholar 

  • Zitová B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21(11):977–1000

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics Engineering, Faculty of Sciences and Technology, Centre for Informatics and Systems, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290, Coimbra, Portugal

    Mariana A. Nogueira, Pedro Henriques Abreu, Pedro Martins & Penousal Machado

  2. IPO-Porto Research Centre (CI-IPOP), Rua Dr. António Bernardino de Almeida, 4200-072, Porto, Portugal

    Hugo Duarte & João Santos

Authors
  1. Mariana A. Nogueira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedro Henriques Abreu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pedro Martins
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Penousal Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hugo Duarte
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. João Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pedro Henriques Abreu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nogueira, M.A., Abreu, P.H., Martins, P. et al. Image descriptors in radiology images: a systematic review. Artif Intell Rev 47, 531–559 (2017). https://doi.org/10.1007/s10462-016-9492-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-016-9492-8

Keywords

Search

Navigation