1. A method to detect microcalcifications in images or mammograms comprising: a pre-processing step comprising: (i) eliminating all extraneous and non-breast artifacts including human introduced labels, radiopaque artifacts; and (ii) down sampling the image or mammograms to reduce computational complexity; a microcalcification segmentation step involving Non Linear Energy Operator (NEO) output based microcalcification segmentation comprising: (i) generating a smoothed NEO (SNEO) for enhancing microcalcification spikes; and (ii) selecting an optimal threshold for detecting suspicious regions containing microcalcification; and a false positive reduction step comprising removing curvilinear structure (CLS) texture appearances of the images or mammograms by reusing the NEO output to enhance computational speed and the quality of microcalcification detection, wherein the false positive reduction step comprises introducing a parameter φ to detect CLS elements, wherein: ∅ = Area ⁢ ⁢ of ⁢ ⁢ individual ⁢ ⁢ element ⁢ ⁢ in ⁢ ⁢ reduced ⁢ ⁢ threshold Area ⁢ ⁢ of ⁢ ⁢ a ⁢ ⁢ individual ⁢ ⁢ element ⁢ ⁢ in ⁢ ⁢ full ⁢ ⁢ threshold involving the same SNEO in the microcalcification segmentation step by reducing average thereshold T avg which is average of local optimal threshold corresponding to each of the SNEO/NEO operated direction output to p% of the average threshold (where p<100), calculating the φ of each element, and if the φ is lower than certain threshold (say T φ ), object is likely to be calcification as its compactness is high and elements having higher φ are treated as the CLS elements and thus discarded.