Image segmentation is a process of splitting up the original image into different parts to obtain the desired object from the image for various computer vision applications. Even though many methods exist in the image processing field, it is a difficult task due to intensity variations, noise, different shapes, and texture. So, image segmentation and quantification are highly dependent on the specific application. There is no unique method to address all the segmentation issues. To address this issue, we performed image segmentation and quantification to obtain the desired object and its features from the image. In the proposed method. The original image is decomposed using the Db6 wavelet family, thresholding, and inverse wavelet transform applied to obtain the segmented image. Image quantification gives the features of the desired object that can be used for various computer vision applications like medical image analysis and object detection. Image quantification and edge detection provide the complete boundaries of the desired object. Simulations were performed on the test image and other real time images. The proposed method results in less relative error (0.078) on the test image; hence, simulations were carried out on the other images to check the performance for broader applications.