Metastatic cancer remains a key task in medical management of the disease, since most cancer mortality rates are accredited to metastatic spread of cancer rather than the primary tumor. Despite the noteworthy improvements in the diagnosis, treatment and clinical management, prediction of prognosis, breast cancer relapse and death rates remain unacceptably high in women worldwide. Magnetic Resonance Imaging serves as an important source in detection, diagnoses and treatment monitoring of Breast Cancer. Image processing techniques like pre-processing using different filters to remove the noise content, image segmentation methods to extract the feature such as major axis length, minor axis length are applied to breast MRI images. A mobile app is developed to send the pre-processed MRI images to the doctors’ smart phone. The aim is to augment the view of the MRI images and interpret the condition of the patient as well as to enrich the overall interpretation process. The objective of the work is the analysis of MRI images which reflect the response of the neoadjuvant therapy administered at each successive stage to breast cancer patients in steps.

Metastatic Breast Cancer, Neoadjuvant therapy, Magnetic Resonance Imaging, Image processing

[1] Yeong Yi An, Sung Hun Kim,2 Bong Joo Kang and Ah Won Lee, Treatment Response Evaluation of Breast Cancer after Neoadjuvant Chemotherapy and Usefulness of the Imaging Parameters of MRI and PET/CT. Journal of Korean Medical Science, June, Vol.30(6), pp. 808–815. 2015 May 13, doi: 10.3346/jkms.2015.30.6.808
[2]URLhttps://www.hopkinsmedicine.org/breast_center/treat ments_services/medical_oncology/neoadjuvant_adjuvant_ch emotherapy.html. Neoadjuvant and Adjuvant Therapy in Breast Cancer.
[3] Mario Mustra, Mislav Grgic. Detection of Areas Containing Microcalcifications in Digital Mammograms. lWSSIP 2014, 21st International Conference on Systems, Signals and Image Processing, 12-15 May 2014, Dubrovnik, Croatia
[4] Bhagyashri G. Patil , Prof. Sanjeev N. Jain. Cancer Cells Detection Using Digital Image Processing Methods. International Journal of Latest Trends in Engineering and Technology (ILJET), Vol 3, Issue 4, March 2014, ISSN: 2278-621X.
[5] Sivaranjini S and Nirmala K. Breast Cancer Response PostNeoadjuvant Chemotherapy Using MRI Measurements. 2017 4th International Conference on Signal Processing, Communications and Networking (ICSCN -2017), 978-1-5090-4307-1/17. 2017.
[6] Arijit Ukil, Soma Bandyoapdhyay, Chetanya Puri, Arpan Pal. ioT Healthcare Analytics: The Importance of Anomaly Detection. 2016 IEEE 30th International Conference on Advanced Information Networking and Applications 1550-445X/16. 2016 IEEE DOI 10.1109/AINA.2016.158.
[7] Luqman Mahmood Mina, Nor Ashidi Mat Isa .Breast Abnormxality Detection in Mammograms using Artificial Neural Network. 2015 IEEE 2015 International Conference on Computer, Communication, and Control Technology (I4CT 2015), April 21 - 23 in Imperial Kuching Hotel, Kuching, Sarawak, Malaysia,978-1-4799-7952, 3-15.
[8] John T. Gohring, Paul S. Dale, Xudong Fan .Detection of HER2 breast cancer biomarker using the opto-fluidic ring resonator biosensor. Elsevier Journal on Sensors and Actuators B Chemical, Vol. 146(1),pp. 226-230, 2010