This paper introduces a novel active contour model for detecting objects in images, based on curve evolution, the Mumford-Shah functional for segmentation, and level sets. The model does not rely on gradient-based edge detection, allowing it to detect objects with boundaries that are not defined by gradients, including smooth or discontinuous boundaries. The energy functional is minimized to find the contour, which is formulated using level sets. The model is solved using a numerical algorithm based on finite differences. Experimental results demonstrate the model's effectiveness on various synthetic and real images, showing its ability to detect objects with different shapes and intensities, even in noisy images. The initial curve can be placed anywhere in the image, and interior contours are automatically detected. The paper also discusses the limitations and potential extensions of the model.This paper introduces a novel active contour model for detecting objects in images, based on curve evolution, the Mumford-Shah functional for segmentation, and level sets. The model does not rely on gradient-based edge detection, allowing it to detect objects with boundaries that are not defined by gradients, including smooth or discontinuous boundaries. The energy functional is minimized to find the contour, which is formulated using level sets. The model is solved using a numerical algorithm based on finite differences. Experimental results demonstrate the model's effectiveness on various synthetic and real images, showing its ability to detect objects with different shapes and intensities, even in noisy images. The initial curve can be placed anywhere in the image, and interior contours are automatically detected. The paper also discusses the limitations and potential extensions of the model.